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Update documentation
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allegroai
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@ -53,14 +53,15 @@ To run a session inside a Docker container, use the `--docker` flag and enter th
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session.
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### Passing requirements
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`clearml-session` can download required Python packages. If the code you are going to execute in the remote session, has
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required packages, they can be specified. If there is a `requirement.txt` file, the file can be attached to the
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command using `--requirements </file/location.txt>`. Alternatively, packages can be manually entered, using `--packages "<package_name>"`
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(for example `--packages "keras" "clearml"`).
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`clearml-session` can download required Python packages.
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A `requirement.txt` file can be attached to the command using `--requirements </file/location.txt>`.
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Alternatively, packages can be manually specified, using `--packages "<package_name>"`
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(for example `--packages "keras" "clearml"`) and they'll be automatically installed.
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### Passing Git credentials
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To send local .git-credentials file to the interactive session, add a `--git-credentials` flag and set it to `True`.
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This way, git references can be tracked, including untracked changes.
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This is helpful if working on private git repositories and allows for seemless cloning and tracking of git references,
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including untracked changes.
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### Re-launching and shutting down sessions
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If a `clearml-session` was launched locally and is still running on a remote machine, users can easily reconnect to it.
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@ -1,3 +0,0 @@
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---
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title: Remote Pycharm Debugging
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---
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@ -503,4 +503,14 @@ clearml-agent daemon --services-mode --queue services --create-queue --docker <d
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Do not enqueue training or inference Tasks into the services queue. They will put an unnecessary load on the server.
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:::
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### Setting Server Credentials
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Self hosted [ClearML Server](deploying_clearml/clearml_server.md) comes by default with a services queue.
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By default, the server is open and does not require username and password, but it can be [password protected](deploying_clearml/clearml_server_security#user-access-security).
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In case it is password protected the services agent will need to be configured with server credentials (associated with a user).
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To do that, set these environment variables on the ClearML Server machine with the appropriate credentials:
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```
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CLEARML_API_ACCESS_KEY
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CLEARML_API_SECRET_KEY
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```
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@ -11,16 +11,14 @@ ClearML Data Management solves two important challenges:
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**We believe Data is not code**. It should not be stored in a git tree, because progress on datasets is not always linear.
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Moreover, it can be difficult and inefficient to find on a git tree the commit associated with a certain version of a dataset.
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The data usage in experiments needs to have high observability and needs to be understood, not just by data scientists.
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`clearml-data` allows to easily create new flexible datasets, from which users can add and remove files. These datasets can
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be retrieved simply from any machine with physical or network access to the data. Additionally, datasets can be set up to
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inherit from other datasets, so data lineages can be created, and users can track when and how their data changes.
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A `clearml-data` dataset is a collection of files, stored on a central storage location (S3 \ GS \ Azure \ Network Storage).
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Datasets can be set up to inherit from other datasets, so data lineages can be created,
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and users can track when and how their data changes.<br/>
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Dataset changes are stored using differentiable storage, meaning a version will store the change-set from its previous dataset parents
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`clearml-data` utilizes existing object storage like S3/GS/Azure and even plain file system shares.
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Datasets are stored in a binary differential format, allowing storage optimization and networking. Local copies
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of datasets are always cached, so the same data never needs to be downloaded twice.
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Local copies of datasets are always cached, so the same data never needs to be downloaded twice.
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When a dataset is pulled it will automatically pull all parent datasets and merge them into one output folder for you to work with
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ClearML-data offers two interfaces:
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- `clearml-data` - CLI utility for creating, uploading, and managing datasets.
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@ -249,10 +247,11 @@ Datasets can be searched by project, name, ID, and tags.
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### Python API
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All API commands should be imported with<br/> `from clearml import Dataset`
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All API commands should be imported with<br/>
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`from clearml import Dataset`
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#### `Dataset.get(dataset_id='<DS_ID>').get_local_copy()`
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#### `Dataset.get(dataset_id=DS_ID).get_local_copy()`
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Returns a path to dataset in cache, and downloads it if it is not already in cache.
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@ -265,7 +264,7 @@ Returns a path to dataset in cache, and downloads it if it is not already in cac
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<br/>
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#### `Dataset.get(dataset_id='<DS_ID>').get_mutable_local_copy()`
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#### `Dataset.get(dataset_id=DS_ID).get_mutable_local_copy()`
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Downloads the dataset to a specific folder (non-cached). If the folder already has contents, specify whether to overwrite
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its contents with the dataset contents.
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@ -32,7 +32,7 @@ title: FAQ
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**Graphs and Logs**
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* [The first log lines are missing from the experiment log tab. Where did they go?](#first-log-lines-missing)
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* [The first log lines are missing from the experiment console tab. Where did they go?](#first-log-lines-missing)
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* [Can I create a graph comparing hyperparameters vs model accuracy?](#compare-graph-parameters)
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* [I want to add more graphs, not just with TensorBoard. Is this supported?](#more-graph-types)
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* [How can I report more than one scatter 2D series on the same plot?](#multiple-scatter2D)
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@ -357,12 +357,12 @@ values are `True`, `False`, and a dictionary for fine-grain control. See [Task.i
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## Graphs and Logs
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**The first log lines are missing from the experiment log tab. Where did they go?** <a id="first-log-lines-missing"></a>
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**The first log lines are missing from the experiment console tab. Where did they go?** <a id="first-log-lines-missing"></a>
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Due to speed/optimization issues, we opted to display only the last several hundred log lines.
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You can always download the full log as a file using the **ClearML Web UI**. In the **ClearML Web UI** **>** experiment
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info panel *>* **RESULTS** tab **>** **LOG** sub-tab, use the **Download full log** feature.
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info panel *>* **RESULTS** tab **>** **CONSOLE** sub-tab, use the **Download full log** feature.
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<br/>
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@ -62,8 +62,8 @@ All the hyperparameters appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**.
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## Log
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## Console
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Output to the console, including the text messages printed from the main Task object and each subprocess appear in **RESULTS** **>** **LOG**.
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Output to the console, including the text messages printed from the main Task object and each subprocess appear in **RESULTS** **>** **CONSOLE**.
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@ -9,7 +9,7 @@ script demonstrates multiple subprocesses interacting and reporting to a main Ta
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which always returns the main Task.
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* The Task in each subprocess reports the following to the main Task:
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* Hyperparameters - Additional, different hyperparameters.
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* Log - Text logged to the console as the Task in each subprocess executes.
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* Console - Text logged to the console as the Task in each subprocess executes.
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* When the script runs, it creates an experiment named `Popen example` which is associated with the `examples` project.
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## Hyperparameters
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@ -28,8 +28,8 @@ Parameter dictionaries appear in **General**.
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## Log
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## Console
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Output to the console, including the text messages from the Task in each subprocess, appear in **RESULTS** **>** **LOG**.
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Output to the console, including the text messages from the Task in each subprocess, appear in **RESULTS** **>** **CONSOLE**.
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@ -18,9 +18,9 @@ which are titled **:monitor: machine**.
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## Log
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## Console
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -23,6 +23,6 @@ Histograms output to TensorBoard. They appear in **RESULTS** **>** **PLOTS**.
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## Logs
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -36,7 +36,7 @@ Histograms for layer density appear in **RESULTS** **>** **PLOTS**.
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## Log
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -63,9 +63,9 @@ The TensorFlow Definitions appear in the **TF_DEFINE** subsection.
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## Log
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## CONSOLE
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Text printed to the console for training appears in **RESULTS** **>** **LOG**.
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Text printed to the console for training appears in **RESULTS** **>** **CONSOLE**.
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@ -40,9 +40,9 @@ TensorFlow Definitions appear in **TF_DEFINE**.
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## Log
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## Console
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -1,66 +0,0 @@
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---
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title: Manual Model Upload
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---
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The [manual_model_upload.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/manual_model_upload.py)
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example demonstrates **ClearML**'s tracking of a manually configured model created with Keras, including:
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* Model checkpoints (snapshots),
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* Hyperparameters
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* Console output.
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When the script runs, it creates an experiment named `Model configuration and upload`, which is associated with the `examples` project.
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Configure **ClearML** for model checkpoint (snapshot) storage in any of the following ways ([debug sample](../../../references/sdk/logger.md#set_default_upload_destination)
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storage is different):
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* In the configuration file, set [default_output_uri](../../../configs/clearml_conf.md#sdkdevelopment).
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* In code, when [initializing a Task](../../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
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* In the **ClearML Web UI**, when [modifying an experiment](../../../webapp/webapp_exp_tuning.md#output-destination).
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## Configuration
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This example shows two ways to connect a configuration, using the [Task.connect_configuration](../../../references/sdk/task.md#connect_configuration)
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method.
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* Connect a configuration file by providing the file's path. **ClearML Server** stores a copy of the file.
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```python
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# Connect a local configuration file
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config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
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config_file = task.connect_configuration(config_file)
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```
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* Create a configuration dictionary and provide the dictionary.
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```python
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model_config_dict = {
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'value': 13.37,
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'dict': {'sub_value': 'string', 'sub_integer': 11},
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'list_of_ints': [1, 2, 3, 4],
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}
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model_config_dict = task.connect_configuration(model_config_dict)
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```
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If the configuration changes, **ClearML** tracks it.
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```python
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model_config_dict['new value'] = 10
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model_config_dict['value'] *= model_config_dict['new value']
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```
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The configuration appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS**.
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## Artifacts
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Model artifacts associated with the experiment appear in the experiment info panel (in the **EXPERIMENTS** tab), and in the model info panel (in the **MODELS** tab).
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The experiment info panel shows model tracking, including the model name and design:
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The model info panel contains the model details, including the model URL, framework, and snapshot locations.
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@ -1,79 +0,0 @@
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---
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title: Manual Model Upload
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---
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The [manual_model_upload.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/manual_model_upload.py)
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example demonstrates **ClearML**'s tracking of a manually configured model created with PyTorch, including model checkpoints
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(snapshots), and output to the console. When the script runs, it creates an experiment named `Model configuration and upload`,
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which is associated with the `examples` project.
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Configure **ClearML** for model checkpoint (snapshot) storage in any of the following ways ([debug sample](../../../references/sdk/logger.md#set_default_upload_destination) storage is different):
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* In the configuration file, set [default_output_uri](../../../configs/clearml_conf.md#sdkdevelopment).
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* In code, when [initializing a Task](../../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
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* In the **ClearML Web UI**, when [modifying an experiment](../../../webapp/webapp_exp_tuning.md#output-destination).
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## Configuration
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This example shows two ways to connect a configuration, using the [Task.connect_configuration](../../../references/sdk/task.md#connect_configuration)
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method.
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* Connect a configuration file by providing the file's path. **ClearML Server** stores a copy of the file.
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```python
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# Connect a local configuration file
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config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
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config_file = task.connect_configuration(config_file)
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```
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* Create a configuration dictionary and plug it into the method.
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```python
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model_config_dict = {
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'value': 13.37,
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'dict': {'sub_value': 'string', 'sub_integer': 11},
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'list_of_ints': [1, 2, 3, 4],
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}
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model_config_dict = task.connect_configuration(model_config_dict)
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```
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If the configuration changes, **ClearML** tracks it.
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```python
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model_config_dict['new value'] = 10
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model_config_dict['value'] *= model_config_dict['new value']
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```
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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in the **MODELS** tab.
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The model info panel contains model details, including:
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* Model design (which is also in the experiment info panel)
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* Label enumeration
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* Model URL
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* Framework
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* Snapshot locations.
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### General model information
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### Model design
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### Label enumeration
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Connect a label enumeration dictionary by calling the [Task.connect_label_enumeration](../../../references/sdk/task.md#connect_label_enumeration)
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method.
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```python
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# store the label enumeration of the training model
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labels = {'background': 0, 'cat': 1, 'dog': 2}
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task.connect_label_enumeration(labels)
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```
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@ -44,8 +44,8 @@ TensorFlow Definitions appear in the **TF_DEFINE** subsection.
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## Log
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## Console
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -71,9 +71,9 @@ These hyperparameters are those in the optimizer Task, where the `HyperParameter
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### Log
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### Console
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All console output from `Hyper-Parameter Optimization` appears in **RESULTS** tab, **LOG** sub-tab.
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All console output from `Hyper-Parameter Optimization` appears in **RESULTS** tab, **CONSOLE** sub-tab.
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@ -43,8 +43,8 @@ TensorFlow Definitions appear in the **TF_DEFINE** subsection.
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## Log
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## Console
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -46,8 +46,8 @@ Parameter dictionaries appear in the **General** subsection.
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## Log
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## Console
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Output to the console appears in **RESULTS** **>** **LOG**.
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Output to the console appears in **RESULTS** **>** **CONSOLE**.
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@ -29,9 +29,9 @@ Parameter dictionaries appear in the **General** subsection.
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## Log
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## Console
|
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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@ -71,6 +71,6 @@ Task.current_task().connect(param)
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## Log
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Output to the console, including the text messages printed from the main Task object and each subprocess, appears in **RESULTS** **>** **LOG**.
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Output to the console, including the text messages printed from the main Task object and each subprocess, appears in **RESULTS** **>** **CONSOLE**.
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@ -41,9 +41,9 @@ page **>** **RESULTS** **>** **SCALARS**.
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## Log
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## Console
|
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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|
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@ -33,9 +33,9 @@ These scalars, along with the resource utilization plots, which are titled **:mo
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## Log
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## Console
|
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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|
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@ -31,7 +31,7 @@ appear in the experiment's page in the **web UI**, under **RESULTS** **>** **SCA
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## Log
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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|
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@ -27,9 +27,9 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
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## Log
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## Console
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|
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
|
||||
Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,86 +0,0 @@
|
||||
---
|
||||
title: Manual Model Upload
|
||||
---
|
||||
|
||||
The [manual_model_upload.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/manual_model_upload.py)
|
||||
example demonstrates **ClearML**'s tracking of a manually configured model created with TensorFlow, including:
|
||||
* Model checkpoints (snapshots)
|
||||
* Hyperparameters
|
||||
* Output to the console.
|
||||
|
||||
When the script runs, it creates an experiment named `Model configuration and upload`, which is associated with the `examples` project.
|
||||
|
||||
Configure **ClearML** for model checkpoints (model snapshot) storage in any of the following ways ([debug sample](../../../references/sdk/logger.md#set_default_upload_destination)
|
||||
storage is different):
|
||||
|
||||
* In the configuration file, set [default_output_uri](../../../configs/clearml_conf.md#sdkdevelopment).
|
||||
* In code, when [initializing a Task](../../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
|
||||
* In the **ClearML Web UI**, when [modifying an experiment](../../../webapp/webapp_exp_tuning.md#output-destination).
|
||||
|
||||
## Configuration
|
||||
|
||||
This example shows two ways to connect a configuration, using the [Task.connect_configuration](../../../references/sdk/task.md#connect_configuration) method:
|
||||
|
||||
* Connect a configuration file by providing the file's path. **ClearML Server** stores a copy of the file.
|
||||
```python
|
||||
# Connect a local configuration file
|
||||
config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
|
||||
config_file = task.connect_configuration(config_file)
|
||||
```
|
||||
|
||||
* Create a configuration dictionary and provide the dictionary.
|
||||
```python
|
||||
model_config_dict = {
|
||||
'value': 13.37,
|
||||
'dict': {'sub_value': 'string', 'sub_integer': 11},
|
||||
'list_of_ints': [1, 2, 3, 4],
|
||||
}
|
||||
model_config_dict = task.connect_configuration(model_config_dict)
|
||||
```
|
||||
|
||||
If the configuration changes, **ClearML** track it.
|
||||
|
||||
```python
|
||||
model_config_dict['new value'] = 10
|
||||
model_config_dict['value'] *= model_config_dict['new value']
|
||||
```
|
||||
|
||||
|
||||
The configuration appears in the experiment's page in the **ClearML web UI**, under **CONFIGURATIONS** **>**
|
||||
**CONFIGURATION OBJECTS**.
|
||||
|
||||
|
||||
|
||||
The output model's configuration appears in **ARTIFACTS** **>** **Output Model**.
|
||||
|
||||
## Artifacts
|
||||
|
||||
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab) and in the info panel
|
||||
of the **MODELS** tab.
|
||||
|
||||
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
|
||||
|
||||
|
||||
|
||||
The model info panel contains the model details, including:
|
||||
* Model design
|
||||
* label enumeration
|
||||
* Model URL
|
||||
* Framework
|
||||
* Snapshot locations.
|
||||
|
||||
### General model information
|
||||
|
||||
|
||||
|
||||
### Label enumeration
|
||||
|
||||
Connect a label enumeration dictionary by calling the [Task.connect_label_enumeration](../../../references/sdk/task.md#connect_label_enumeration) method.
|
||||
|
||||
```python
|
||||
# store the label enumeration of the training model
|
||||
labels = {'background': 0, 'cat': 1, 'dog': 2}
|
||||
task.connect_label_enumeration(labels)
|
||||
```
|
||||
|
||||
|
||||
@ -31,8 +31,8 @@ In the **ClearML Web UI**, the PR Curve summaries appear in the experiment's pag
|
||||
|
||||
|
||||
|
||||
## Log
|
||||
## Console
|
||||
|
||||
All other console output appears in **RESULTS** **>** **LOG**.
|
||||
All other console output appears in **RESULTS** **>** **CONSOLE**.
|
||||
|
||||
|
||||
|
||||
@ -29,9 +29,9 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
|
||||
|
||||
|
||||
|
||||
## Log
|
||||
## Console
|
||||
|
||||
All console output appears in **RESULTS** **>** **LOG**.
|
||||
All console output appears in **RESULTS** **>** **CONSOLE**.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -31,9 +31,9 @@ The feature importance plot and tree plot appear in the project's page in the **
|
||||
|
||||
|
||||
|
||||
## Log
|
||||
## Console
|
||||
|
||||
All other console output appear in **RESULTS** **>** **LOG**.
|
||||
All other console output appear in **RESULTS** **>** **CONSOLE**.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -14,7 +14,7 @@ The **ClearML PyCharm plugin** enables syncing a local execution configuration t
|
||||
|
||||
**To install the ClearML PyCharm plugin, do the following:**
|
||||
|
||||
1. Download the latest plugin version from the [Releases page](https://github.com/allegroai/trains-pycharm-plugin/releases).
|
||||
1. Download the latest plugin version from the [Releases page](https://github.com/allegroai/clearml-pycharm-plugin/releases).
|
||||
|
||||
1. Install the plugin in PyCharm from local disk:
|
||||
|
||||
|
||||
@ -239,7 +239,7 @@ After extending the Python experiment script, run it and view the results in the
|
||||
including the file path, size, hash, metadata, and a preview.
|
||||
1. In the **OTHER** section, click **Loss**. The uploaded numpy array appears, including its related information.
|
||||
1. Click the **RESULTS** tab.
|
||||
1. Click the **LOG** sub-tab, and see the debugging message showing the Pandas DataFrame sample.
|
||||
1. Click the **CONSOLE** sub-tab, and see the debugging message showing the Pandas DataFrame sample.
|
||||
1. Click the **SCALARS** sub-tab, and see the scalar plots for epoch logging loss.
|
||||
1. Click the **PLOTS** sub-tab, and see the confusion matrix and histogram.
|
||||
|
||||
|
||||
@ -54,7 +54,7 @@ It shows the server links are:
|
||||
|
||||
|
||||
|
||||
The Jupyter Note Server console output appears in **RESULTS** **>** **LOG**, including log entries for the notebooks run
|
||||
The Jupyter Note Server console output appears in **RESULTS** **>** **CONSOLE**, including log entries for the notebooks run
|
||||
on the server.
|
||||
|
||||
To test the Jupyter Notebook, we ran a notebook named audio_preprocessing_example.ipynb. The log shows it was saved:
|
||||
|
||||
BIN
docs/img/fundamentals_hpo_summary.png
Normal file
BIN
docs/img/fundamentals_hpo_summary.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 101 KiB |
@ -2,43 +2,17 @@
|
||||
title: Storage
|
||||
---
|
||||
|
||||
import ImageSwitcher from '/ImageSwitcher.js';
|
||||
|
||||
ClearML is able to interface with the most popular storage solutions in the market for storing model checkpoints, artifacts
|
||||
and charts.
|
||||
|
||||
Supported storage mediums include:
|
||||
|
||||
<div className="supported-storages">
|
||||
<div>
|
||||
<img src="/icons/ico-local-and-shared.svg" alt="Storage icon" />
|
||||
Local and shared folders
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-aws-s3.svg" alt="Storage icon" />
|
||||
S3 buckets
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-google-cloud-storage.svg" alt="Storage icon" />
|
||||
Google Cloud Storage
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-azure-storage.svg" alt="Storage icon" />
|
||||
Azure Storage
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-nas.svg" alt="Storage icon" />
|
||||
http(s)
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-minio.svg" alt="Storage icon" />
|
||||
minio
|
||||
</div>
|
||||
<div>
|
||||
<img src="/icons/ico-ceph.svg" alt="Storage icon" />
|
||||
ceph
|
||||
</div>
|
||||
</div>
|
||||
|
||||
---
|
||||
<ImageSwitcher alt="ClearML Supported Storage"
|
||||
lightImageSrc="/icons/ClearML_Supported_Storage--on-light.png"
|
||||
darkImageSrc="/icons/ClearML_Supported_Storage--on-dark.png"
|
||||
/>
|
||||
|
||||
:::note
|
||||
Once uploading an object to a storage medium, each machine that uses the object must have access to it.
|
||||
|
||||
5
docs/references/api/definitions.md
Normal file
5
docs/references/api/definitions.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: API Definitions
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/api/endpoints.md
Normal file
5
docs/references/api/endpoints.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: REST API
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: PipelineController
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/automation_job_clearmljob.md
Normal file
5
docs/references/sdk/automation_job_clearmljob.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: ClearmlJob
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/dataset.md
Normal file
5
docs/references/sdk/dataset.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: Dataset
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: OptimizerBOHB
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/hpo_optimization_gridsearch.md
Normal file
5
docs/references/sdk/hpo_optimization_gridsearch.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: GridSearch
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: HyperParameterOptimizer
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/hpo_optimization_randomsearch.md
Normal file
5
docs/references/sdk/hpo_optimization_randomsearch.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: RandomSearch
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/hpo_optuna_optuna_optimizeroptuna.md
Normal file
5
docs/references/sdk/hpo_optuna_optuna_optimizeroptuna.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: OptimizerOptuna
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: DiscreteParameterRange
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/hpo_parameters_parameterset.md
Normal file
5
docs/references/sdk/hpo_parameters_parameterset.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: ParameterSet
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: UniformIntegerParameterRange
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: UniformParameterRange
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/logger.md
Normal file
5
docs/references/sdk/logger.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: Logger
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/model_inputmodel.md
Normal file
5
docs/references/sdk/model_inputmodel.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: InputModel
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/model_model.md
Normal file
5
docs/references/sdk/model_model.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: Model
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/model_outputmodel.md
Normal file
5
docs/references/sdk/model_outputmodel.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: OutputModel
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: AwsAutoScaler
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/services_monitor.md
Normal file
5
docs/references/sdk/services_monitor.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: Monitor
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/storage.md
Normal file
5
docs/references/sdk/storage.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: StorageManager
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
5
docs/references/sdk/task.md
Normal file
5
docs/references/sdk/task.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
title: Task
|
||||
---
|
||||
|
||||
**AutoGenerated PlaceHolder**
|
||||
@ -9,7 +9,7 @@ including:
|
||||
* [Configuration](#configuration) - Hyperparameters, user properties, and configuration objects.
|
||||
* [Artifacts](#artifacts) - Input model, output model, model snapshot locations, other artifacts.
|
||||
* [General information](#general-information) - Information about the experiment, for example: the experiment start, create, and last update times and dates, user creating the experiment, and its description.
|
||||
* [Logs](#log) - stdout, stderr, output to the console from libraries, and **ClearML** explicit reporting.
|
||||
* [Console](#console) - stdout, stderr, output to the console from libraries, and **ClearML** explicit reporting.
|
||||
* [Scalars](#scalars) - Metric plots.
|
||||
* [Plots](#other-plots) - Other plots and data, for example: Matplotlib, Plotly, and **ClearML** explicit reporting.
|
||||
* [Debug samples](#debug-samples) - Images, audio, video, and HTML.
|
||||
@ -332,9 +332,9 @@ General experiment details appear in the **INFO** tab. This includes information
|
||||
|
||||
|
||||
|
||||
### Log
|
||||
### Console
|
||||
|
||||
The complete experiment log containing everything printed to stdout and strerr appears in the **LOG** tab. The full log
|
||||
The complete experiment log containing everything printed to stdout and strerr appears in the **CONSOLE** tab. The full log
|
||||
is downloadable. To view the end of the log, click **Jump to end**.
|
||||
|
||||
<details className="cml-expansion-panel screenshot">
|
||||
|
||||
@ -186,13 +186,13 @@ module.exports = {
|
||||
sidebarPath: require.resolve('./sidebars.js'),
|
||||
// Please change this to your repo.
|
||||
editUrl:
|
||||
'https://github.com/allegroai/clearml_docs/edit/master/website/',
|
||||
'https://github.com/allegroai/clearml-docs/edit/main/',
|
||||
},
|
||||
API: {
|
||||
sidebarPath: require.resolve('./sidebars.js'),
|
||||
// Please change this to your repo.
|
||||
editUrl:
|
||||
'https://github.com/allegroai/clearml_docs/edit/master/website/',
|
||||
'https://github.com/allegroai/clearml-docs/edit/main/',
|
||||
},
|
||||
blog: {
|
||||
blogTitle: 'ClearML Tutorials',
|
||||
@ -202,7 +202,7 @@ module.exports = {
|
||||
showReadingTime: true,
|
||||
// Please change this to your repo.
|
||||
editUrl:
|
||||
'https://github.com/allegroai/clearml_docs/edit/master/website/tutorials/',
|
||||
'https://github.com/allegroai/clearml-docs/edit/main/tutorials/',
|
||||
},
|
||||
theme: {
|
||||
customCss: require.resolve('./src/css/custom.css'),
|
||||
|
||||
@ -63,12 +63,11 @@ module.exports = {
|
||||
{'Autokeras': ['guides/frameworks/autokeras/integration_autokeras', 'guides/frameworks/autokeras/autokeras_imdb_example']},
|
||||
{'FastAI': ['guides/frameworks/fastai/fastai_with_tensorboard']},
|
||||
{
|
||||
'Keras': ['guides/frameworks/keras/allegro_clearml_keras_tb_example', 'guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard',
|
||||
'guides/frameworks/keras/manual_model_upload']
|
||||
'Keras': ['guides/frameworks/keras/allegro_clearml_keras_tb_example', 'guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']
|
||||
},
|
||||
{'Matplotlib': ['guides/frameworks/matplotlib/allegro_clearml_matplotlib_example', 'guides/frameworks/matplotlib/matplotlib_example']},
|
||||
{
|
||||
'Pytorch': ['guides/frameworks/pytorch/manual_model_upload', 'guides/frameworks/pytorch/pytorch_distributed_example', 'guides/frameworks/pytorch/pytorch_matplotlib',
|
||||
'Pytorch': ['guides/frameworks/pytorch/pytorch_distributed_example', 'guides/frameworks/pytorch/pytorch_matplotlib',
|
||||
'guides/frameworks/pytorch/pytorch_mnist', 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/pytorch/pytorch_tensorboardx',
|
||||
'guides/frameworks/pytorch/tensorboard_toy_pytorch']
|
||||
},
|
||||
@ -84,12 +83,12 @@ module.exports = {
|
||||
{'Scikit-Learn': ['guides/frameworks/scikit-learn/sklearn_joblib_example', 'guides/frameworks/scikit-learn/sklearn_matplotlib_example']},
|
||||
{'TensorboardX': ['guides/frameworks/tensorboardx/tensorboardx']},
|
||||
{
|
||||
'Tensorflow': ['guides/frameworks/tensorflow/manual_model_upload', 'guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
|
||||
'Tensorflow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
|
||||
'guides/frameworks/tensorflow/tensorflow_mnist', 'guides/frameworks/tensorflow/integration_keras_tuner']
|
||||
},
|
||||
{'XGboost': ['guides/frameworks/xgboost/xgboost_sample']}
|
||||
]},
|
||||
{'IDEs': ['guides/ide/remote_jupyter_tutorial']},
|
||||
{'IDEs': ['guides/ide/remote_jupyter_tutorial', 'guides/ide/integration_pycharm']},
|
||||
{'Optimization': ['guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt']},
|
||||
{'Pipelines': ['guides/pipeline/pipeline_controller']},
|
||||
|
||||
|
||||
@ -181,17 +181,17 @@ html[data-theme="light"] .hero .button.button--primary{
|
||||
|
||||
/* header social icons */
|
||||
.header-ico--github {
|
||||
background-image: url(/img/ico-github.svg);
|
||||
background-image: url(docs/latest/img/ico-github.svg);
|
||||
margin-right: calc(var(--ifm-navbar-item-padding-horizontal) * 2);
|
||||
}
|
||||
.header-ico--twitter {
|
||||
background-image: url(/img/ico-twitter.svg);
|
||||
background-image: url(docs/latest/img/ico-twitter.svg);
|
||||
}
|
||||
.header-ico--youtube {
|
||||
background-image: url(/img/ico-youtube.svg);
|
||||
background-image: url(docs/latest/img/ico-youtube.svg);
|
||||
}
|
||||
.header-ico--slack {
|
||||
background-image: url(/img/ico-slack.svg);
|
||||
background-image: url(docs/latest/img/ico-slack.svg);
|
||||
}
|
||||
.header-ico {
|
||||
width:24px;
|
||||
@ -255,7 +255,7 @@ html[data-theme="light"] .hero .button.button--primary{
|
||||
|
||||
|
||||
html[data-theme="light"] [class^="sidebarLogo"] {
|
||||
background: url(/img/logo--on-light.svg) 1rem center no-repeat;
|
||||
background: url(docs/latest/img/logo--on-light.svg) 1rem center no-repeat;
|
||||
background-size: 6rem;
|
||||
}
|
||||
html[data-theme="light"] [class^="sidebarLogo"] > img {
|
||||
@ -283,7 +283,7 @@ html[data-theme="dark"] .navbar-sidebar {
|
||||
background-color: #141722;
|
||||
}
|
||||
html[data-theme="light"] .navbar-sidebar .navbar__brand {
|
||||
background: url(/img/logo--on-light.svg) 0 center no-repeat;
|
||||
background: url(docs/latest/img/logo--on-light.svg) 0 center no-repeat;
|
||||
background-size: 5.5rem;
|
||||
}
|
||||
html[data-theme="light"] .navbar-sidebar .navbar__logo {
|
||||
@ -315,16 +315,16 @@ html[data-theme="dark"] .navbar-sidebar .menu__link.header-ico--slack:before {
|
||||
|
||||
|
||||
.navbar-sidebar .menu__link.header-ico--slack:hover {
|
||||
background: url(/img/ico-slack.svg) no-repeat center;
|
||||
background: url(docs/latest/img/ico-slack.svg) no-repeat center;
|
||||
}
|
||||
.navbar-sidebar .menu__link.header-ico--twitter:hover {
|
||||
background: url(/img/ico-twitter.svg) no-repeat center;
|
||||
background: url(docs/latest/img/ico-twitter.svg) no-repeat center;
|
||||
}
|
||||
.navbar-sidebar .menu__link.header-ico--github:hover {
|
||||
background: url(/img/ico-github.svg) no-repeat center;
|
||||
background: url(docs/latest/img/ico-github.svg) no-repeat center;
|
||||
}
|
||||
.navbar-sidebar .menu__link.header-ico--youtube:hover {
|
||||
background: url(/img/ico-youtube.svg) no-repeat center;
|
||||
background: url(docs/latest/img/ico-youtube.svg) no-repeat center;
|
||||
}
|
||||
|
||||
.menu__link.header-ico {
|
||||
@ -414,22 +414,22 @@ html[data-theme="dark"] .footer__copyright {
|
||||
/* social links icons */
|
||||
.footer__link-item[href*="slack"] {
|
||||
padding-left: 1.4rem;
|
||||
background: url(/img/ico-slack.svg) no-repeat left center;
|
||||
background: url(docs/latest/img/ico-slack.svg) no-repeat left center;
|
||||
background-size: 1rem;
|
||||
}
|
||||
.footer__link-item[href*="youtube"] {
|
||||
padding-left: 1.4rem;
|
||||
background: url(/img/ico-youtube.svg) no-repeat left center;
|
||||
background: url(docs/latest/img/ico-youtube.svg) no-repeat left center;
|
||||
background-size: 1rem;
|
||||
}
|
||||
.footer__link-item[href*="twitter"] {
|
||||
padding-left: 1.4rem;
|
||||
background: url(/img/ico-twitter.svg) no-repeat left center;
|
||||
background: url(docs/latest/img/ico-twitter.svg) no-repeat left center;
|
||||
background-size: 1rem;
|
||||
}
|
||||
.footer__link-item[href*="stackoverflow"] {
|
||||
padding-left: 1.4rem;
|
||||
background: url(/img/ico-stackoverflow.svg) no-repeat left center;
|
||||
background: url(docs/latest/img/ico-stackoverflow.svg) no-repeat left center;
|
||||
background-size: 1rem;
|
||||
}
|
||||
|
||||
@ -442,8 +442,8 @@ html[data-theme="light"] .footer__link-item[href*="stackoverflow"] {
|
||||
|
||||
|
||||
/* ===MARKDOWN=== */
|
||||
.markdown img[src*="png"],
|
||||
.markdown img[src*="gif"] {
|
||||
.markdown img.medium-zoom-image[src*="png"],
|
||||
.markdown img.medium-zoom-image[src*="gif"] {
|
||||
border:1px solid #2c3246;
|
||||
}
|
||||
.getting-started-buttons > .col {
|
||||
@ -582,7 +582,7 @@ html[data-theme="light"] .icon {
|
||||
height: 24px;
|
||||
transform: rotate(0);
|
||||
transition: 0.25s;
|
||||
background: url(/icons/ico-chevron-down.svg) no-repeat center;
|
||||
background: url(docs/latest/icons/ico-chevron-down.svg) no-repeat center;
|
||||
}
|
||||
|
||||
/* expansion content */
|
||||
@ -594,23 +594,23 @@ html[data-theme="light"] .icon {
|
||||
/* icon types */
|
||||
/* -> info */
|
||||
.cml-expansion-panel.info .cml-expansion-panel-summary:before {
|
||||
background-image: url(/icons/ico-info-circle.svg);
|
||||
background-image: url(docs/latest/icons/ico-info-circle.svg);
|
||||
}
|
||||
/* -> tips */
|
||||
.cml-expansion-panel.tips .cml-expansion-panel-summary:before {
|
||||
background-image: url(/icons/ico-tips.svg);
|
||||
background-image: url(docs/latest/icons/ico-tips.svg);
|
||||
}
|
||||
/* -> alert */
|
||||
.cml-expansion-panel.alert .cml-expansion-panel-summary:before {
|
||||
background-image: url(/icons/ico-alert.svg);
|
||||
background-image: url(docs/latest/icons/ico-alert.svg);
|
||||
}
|
||||
/* -> screenshot */
|
||||
.cml-expansion-panel.screenshot .cml-expansion-panel-summary:before {
|
||||
background-image: url(/icons/ico-image.svg);
|
||||
background-image: url(docs/latest/icons/ico-image.svg);
|
||||
}
|
||||
/* -> configuration */
|
||||
.cml-expansion-panel.configuration .cml-expansion-panel-summary:before {
|
||||
background-image: url(/icons/ico-config.svg);
|
||||
background-image: url(docs/latest/icons/ico-config.svg);
|
||||
}
|
||||
|
||||
/* light mode */
|
||||
@ -701,48 +701,12 @@ html[data-theme="dark"] .alert--info {
|
||||
|
||||
/* show visual identification for external links */
|
||||
.markdown a[href^="https://"] {
|
||||
background: url(/icons/ico-open-in-new.svg) center right no-repeat;
|
||||
background: url(docs/latest/icons/ico-open-in-new.svg) center right no-repeat;
|
||||
background-size: 0.9rem;
|
||||
margin-right: 0.4rem;
|
||||
padding-right: 1rem;
|
||||
}
|
||||
|
||||
|
||||
/* supported storages */
|
||||
|
||||
.supported-storages {
|
||||
margin: 1rem auto;
|
||||
padding: 0;
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
justify-content: center;
|
||||
gap: 0.5rem;
|
||||
.markdown .img-swt {
|
||||
margin-bottom: 2.4rem;
|
||||
}
|
||||
.supported-storages > div {
|
||||
padding: 1.6rem 1rem 0;
|
||||
width: 8rem;
|
||||
min-height: 8rem;
|
||||
text-align: center;
|
||||
font-size: 0.875rem;
|
||||
font-weight: 500;
|
||||
line-height: 1.2;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
background-color: var(--ifm-toc-background-color);
|
||||
border-radius: 1rem;
|
||||
}
|
||||
html[data-theme="light"] .supported-storages > div {
|
||||
background-color: var(--ifm-color-secondary);
|
||||
}
|
||||
.supported-storages img {
|
||||
width: 48px;
|
||||
height: 48px;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
@media only screen and (min-width: 767px) {
|
||||
.supported-storages {
|
||||
width: 80%;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
BIN
static/icons/ClearML_Supported_Storage--on-dark.png
Normal file
BIN
static/icons/ClearML_Supported_Storage--on-dark.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 28 KiB |
BIN
static/icons/ClearML_Supported_Storage--on-light.png
Normal file
BIN
static/icons/ClearML_Supported_Storage--on-light.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 27 KiB |
Loading…
Reference in New Issue
Block a user