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27
docs/apps/clearml_session.md
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@@ -13,8 +13,8 @@ in the UI and send it for long-term training on a remote machine.
## What Does ClearML Session Do?
`clearml-session` is a feature that allows to launch a session of JupyterLab and VS Code, and to execute code on a remote
machine that better meets resource needs. With this feature, local links are provided, which can be used to access
JupyterLab and VS Code on a remote machine over a secure and encrypted SSH connection. By default, the JupyterLab and
machine that better meets resource needs. This feature provides local links to access JupyterLab and VS Code on a
remote machine over a secure and encrypted SSH connection. By default, the JupyterLab and
VS Code remote sessions use ports 8878 and 8898 respectively.
<details className="cml-expansion-panel screenshot">
@@ -40,7 +40,7 @@ VS Code remote sessions use ports 8878 and 8898 respectively.
## How it Works
ClearML allows to leverage a resource (e.g. GPU or CPU machine) by utilizing the [ClearML Agent](../clearml_agent).
A ClearML Agent will run on a target machine, and ClearML Session will instruct it to execute the Jupyter / VS Code
A ClearML Agent runs on a target machine, and ClearML Session instructs it to execute the Jupyter / VS Code
server to develop remotely.
After entering a `clearml-session` command with all specifications:
@@ -51,8 +51,8 @@ After entering a `clearml-session` command with all specifications:
launches it.
1. Once the agent finishes the initial setup of the interactive Task, the local `cleaml-session` connects to the host
machine via SSH, and tunnels both SSH and JupyterLab over the SSH connection. If a specific Docker was specified, the
JupyterLab environment will run inside the Docker.
machine via SSH, and tunnels both SSH and JupyterLab over the SSH connection. If a Docker is specified, the
JupyterLab environment runs inside the Docker.
1. The CLI outputs access links to the remote JupyterLab and VS Code sessions:
@@ -73,14 +73,15 @@ To run a session inside a Docker container, use the `--docker` flag and enter th
session.
### Installing Requirements
`clearml-session` can install required Python packages when setting up the remote environment. A `requirement.txt` file
can be attached to the command using `--requirements </file/location.txt>`.
Alternatively, packages can be manually specified, using `--packages "<package_name>"`
`clearml-session` can install required Python packages when setting up the remote environment.
Specify requirements in one of the following ways:
* Attach a `requirement.txt` file to the command using `--requirements </file/location.txt>`.
* Manually specify packages using `--packages "<package_name>"`
(for example `--packages "keras" "clearml"`), and they'll be automatically installed.
### Accessing a Git Repository
To access a git repository remotely, add a `--git-credentials` flag and set it to `true`, so the local .git-credentials
file will be sent to the interactive session. This is helpful if working on private git repositories, and it allows for seamless
To access a git repository remotely, add a `--git-credentials` flag and set it to `true`, so the local `.git-credentials`
file is sent to the interactive session. This is helpful if working on private git repositories, and it allows for seamless
cloning and tracking of git references, including untracked changes.
### Re-launching and Shutting Down Sessions
@@ -101,11 +102,11 @@ Active sessions:
Connect to session [0-1] or 'N' to skip
```
To shut down a remote session, which will free the `clearml-agent` and close the CLI, enter "Shutdown". If a session
To shut down a remote session, which frees the `clearml-agent` and closes the CLI, enter "Shutdown". If a session
is shutdown, there is no option to reconnect to it.
### Connecting to an Existing Session
If a `clearml-session` is running remotely, it's possible to continue working on the session from any machine.
If a `clearml-session` is running remotely, you can continue working on the session from any machine.
When `clearml-session` is launched, it initializes a task with a unique ID in the ClearML Server.
To connect to an existing session:
@@ -116,7 +117,7 @@ To connect to an existing session:
### Starting a Debugging Session
Previously executed experiments in the ClearML system can be debugged on a remote interactive session.
You can debug previously executed experiments registered in the ClearML system on a remote interactive session.
Input into `clearml-session` the ID of a Task to debug, then `clearml-session` clones the experiment's git repository and
replicates the environment on a remote machine. Then the code can be interactively executed and debugged on JupyterLab / VS Code.

6
docs/apps/clearml_task.md
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@@ -6,7 +6,7 @@ ClearML Task is ClearML's Zero Code Integration Module. Using only the command l
you can easily track your work and integrate ClearML with your existing code.
`clearml-task` automatically integrates ClearML into any script or **any** python repository. `clearml-task` has the option
to send the task to a queue, where a **ClearML Agent** listening to the queue will fetch the task and execute it on a
to send the task to a queue, where a ClearML Agent assigned to the queue fetches the task and executes it on a
remote or local machine. It's even possible to provide command line arguments and provide Python module dependencies and requirements.txt file!
## How Does ClearML Task Work?
@@ -14,8 +14,8 @@ remote or local machine. It's even possible to provide command line arguments an
1. Execute `clearml-task`, pointing it to your script or repository, and optionally an execution queue.
1. `clearml-task` does its magic! It creates a new experiment on the [ClearML Server](../deploying_clearml/clearml_server.md),
and, if a queue was specified, it sends the experiment to the queue to be fetched and executed by a **ClearML Agent**.
1. The command line will provide you with a link to your task's page in the ClearML web UI,
where you will be able to view the task's details.
1. The command line provides you with a link to your task's page in the ClearML web UI,
where you can view the task's details.
## Features and Options
### Docker