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@ -4,7 +4,7 @@
## **`clearml-session` </br> CLI for launching JupyterLab / VSCode / SSH on a remote machine** ## **`clearml-session` </br> CLI for launching JupyterLab / VSCode / SSH on a remote machine**
## 🔥 NEW in version `0.13` [Workspace Syncing](#store-and-synchronize-interactive-session-workspace) 🚀 ## 🔥 NEW in version `0.13` [Workspace Syncing](#-store-and-synchronize-interactive-session-workspace) 🚀
[![GitHub license](https://img.shields.io/github/license/allegroai/clearml-session.svg)](https://img.shields.io/github/license/allegroai/clearml-session.svg) [![GitHub license](https://img.shields.io/github/license/allegroai/clearml-session.svg)](https://img.shields.io/github/license/allegroai/clearml-session.svg)
@ -30,7 +30,7 @@ interactive usage (i.e., development)
- ClearML will start monitoring machine performance, allowing DevOps to detect stale instances and spin them down - ClearML will start monitoring machine performance, allowing DevOps to detect stale instances and spin them down
- NEW 🔥 Kubernetes support, develop directly inside your pods! No kubectl required! - NEW 🔥 Kubernetes support, develop directly inside your pods! No kubectl required!
Read more about `clearml-agent` and interactive sessions [here](https://clear.ml/docs/latest/docs/clearml_agent/#kubernetes) Read more about `clearml-agent` and interactive sessions [here](https://clear.ml/docs/latest/docs/clearml_agent/#kubernetes)
- NEW 🎉 Automatically store & sync your [interactive session workspace](#store-and-synchronize-interactive-session-workspace). - NEW 🎉 Automatically store & sync your [interactive session workspace](#-store-and-synchronize-interactive-session-workspace).
`clearml-session` will automatically create a snapshot of your entire workspace when shutting it down, `clearml-session` will automatically create a snapshot of your entire workspace when shutting it down,
and later restore into a new session on a different remote machine and later restore into a new session on a different remote machine
@ -44,7 +44,7 @@ and later restore into a new session on a different remote machine
4. Scale-out development to multiple clouds, assign development machines on AWS/GCP/Azure in a seamless way 4. Scale-out development to multiple clouds, assign development machines on AWS/GCP/Azure in a seamless way
## Prerequisites: ## Prerequisites:
* **An SSH client installed on your machine** - To verify open your terminal and execute `ssh`, if you did not receive an error, we are good to go. * **An SSH client installed on your machine** - To verify, open your terminal and execute `ssh`. If you did not receive an error, we are good to go.
* At least one `clearml-agent` running on a remote host. See installation [details](https://github.com/allegroai/clearml-agent). * At least one `clearml-agent` running on a remote host. See installation [details](https://github.com/allegroai/clearml-agent).
Supported OS: MacOS, Windows, Linux Supported OS: MacOS, Windows, Linux
@ -62,17 +62,17 @@ connection without breaking any network sockets!
This means that if the network connection is unstable, you can refresh This means that if the network connection is unstable, you can refresh
the base SSH network tunnel, without breaking JupyterLab/VSCode-server or your own SSH connection the base SSH network tunnel, without breaking JupyterLab/VSCode-server or your own SSH connection
(e.h. debugging over SSH with PyCharm) (e.g. debugging over SSH with PyCharm)
--- ---
## ⚡ How to use: Interactive Session ## ⚡ How to use: Interactive Session
1. run `clearml-session` 1. Run `clearml-session`
2. select the requested queue (resource) 2. Select the requested queue (resource)
3. wait until a machine is up and ready 3. Wait until a machine is up and ready
4. click on the link to the remote JupyterLab/VSCode OR connect with the provided SSH details 4. Click on the link to the remote JupyterLab/VSCode OR connect with the provided SSH details
**Notice! You can also**: Select a **docker image** to execute in, install required **python packages**, run **bash script**, **Notice! You can also**: Select a **docker image** to execute in, install required **python packages**, run **bash script**,
pass **git credentials**, etc. pass **git credentials**, etc.
@ -88,7 +88,7 @@ pip install clearml-session
clearml-session --docker nvcr.io/nvidia/pytorch:20.11-py3 --git-credentials clearml-session --docker nvcr.io/nvidia/pytorch:20.11-py3 --git-credentials
``` ```
Wait for the machine to spin up: Wait for the machine to spin up.
Expected CLI output would look something like: Expected CLI output would look something like:
``` console ``` console
Creating new session Creating new session
@ -120,9 +120,9 @@ Enter "r" (or "reconnect") to reconnect the session (for example after suspend)
or "Shutdown" to shut down remote interactive session or "Shutdown" to shut down remote interactive session
``` ```
Click on the **Jupyter Lab** link (http://localhost:8878/?token=xyz) Click on the **Jupyter Lab** link (http://localhost:8878/?token=xyz),
Or **VScode** (running inside your remote container) (http://localhost:8898/) or **VScode** (running inside your remote container) (http://localhost:8898/),
Or drop into **SSH** shell by typying `shell` or drop into **SSH** shell by typing `shell`.
Open your terminal, clone your code & start working :) Open your terminal, clone your code & start working :)
@ -143,7 +143,7 @@ When you want to reconnect to it, execute:
clearml-session clearml-session
``` ```
Then press "Y" (or enter) to reconnect to the already running session Then press "Y" (or enter) to reconnect to the already running session:
``` console ``` console
clearml-session - launch interactive session clearml-session - launch interactive session
Checking previous session Checking previous session
@ -152,8 +152,8 @@ Connect to active session id=3d38e738c5ff458a9ec465e77e19da23 [Y]/n?
### ⏻ Shutting down a remote session ### ⏻ Shutting down a remote session
On the `clearml-session` CLI terminal, enter 'shutdown' (case-insensitive) On the `clearml-session` CLI terminal, enter 'shutdown' (case-insensitive).
It will shut down the remote session, free the resource and close the CLI It will shut down the remote session, free the resource and close the CLI:
```console ```console
Enter "r" (or "reconnect") to reconnect the session (for example after suspend) Enter "r" (or "reconnect") to reconnect the session (for example after suspend)
@ -168,7 +168,7 @@ Remote session shutdown
Goodbye Goodbye
``` ```
You can also use the CLI to shut down a specific clearml interactive session You can also use the CLI to shut down a specific clearml interactive session:
```bash ```bash
clearml-session shutdown --id <session_id> clearml-session shutdown --id <session_id>
@ -177,14 +177,14 @@ clearml-session shutdown --id <session_id>
### 🔗 Connecting to a running interactive session from a different machine ### 🔗 Connecting to a running interactive session from a different machine
Continue working on an interactive session from **any** machine. Continue working on an interactive session from **any** machine.
In the `clearml` web UI, go to DevOps project, and find your interactive session. In the `clearml` web UI, go to the DevOps project, and find your interactive session.
Click on the ID button next to the Task name, and copy the unique ID. Click on the ID button next to the Task name, and copy the unique ID.
```bash ```bash
clearml-session --attach <session_id> clearml-session --attach <session_id>
``` ```
Click on the JupyterLab/VSCode link, or connect directly to the SSH session Click on the JupyterLab/VSCode link, or connect directly to the SSH session.
> ✨ **TIP**: You can work & debug your colleagues code and workspace by sharing the `session id` > ✨ **TIP**: You can work & debug your colleagues code and workspace by sharing the `session id`
> and connect to the same remote container together with `--attach` > and connect to the same remote container together with `--attach`
@ -194,15 +194,15 @@ Click on the JupyterLab/VSCode link, or connect directly to the SSH session
Specify the remote workspace root-folder by adding `--store-workspace ~/workspace` to the command line. Specify the remote workspace root-folder by adding `--store-workspace ~/workspace` to the command line.
In the remote session container, put all your code / data under the `~/workspace` directory. In the remote session container, put all your code / data under the `~/workspace` directory.
When your session is shut down, the workspace folder will be automatically package and stored on the clearml file server. When your session is shut down, the workspace folder will be automatically packaged and stored on the clearml file server.
In your next `clearml-session` execution specify again `--store-workspace ~/workspace` and clearml-session In your next `clearml-session` execution, specify again `--store-workspace ~/workspace` and clearml-session
will grab the previous workspace snapshot and restore it into the new remote container in `~/workspace`. will grab the previous workspace snapshot and restore it into the new remote container in `~/workspace`.
```bash ```bash
clearml-session --store-workspace ~/workspace --docker python:3.10-bullseye clearml-session --store-workspace ~/workspace --docker python:3.10-bullseye
``` ```
To continue the last aborted session and restore the workspace To continue the last aborted session and restore the workspace:
```bash ```bash
clearml-session --store-workspace ~/workspace --docker python:3.10-bullseye clearml-session --store-workspace ~/workspace --docker python:3.10-bullseye
@ -219,7 +219,7 @@ Restore workspace from session id=01bf86f038314434878b2413343ba746 'interactive_
Restoring workspace from previous session id=01bf86f038314434878b2413343ba746 Restoring workspace from previous session id=01bf86f038314434878b2413343ba746
``` ```
To continue a **specific** session ID and restore its workspace To continue a **specific** session ID and restore its workspace:
```bash ```bash
clearml-session --continue-session <session_id> --store-workspace ~/workspace --docker python:3.10-bullseye clearml-session --continue-session <session_id> --store-workspace ~/workspace --docker python:3.10-bullseye
@ -232,7 +232,7 @@ specify the file or directory with `--upload-files /mnt/data/stuff`.
The entire content of the directory / file will be copied into your remote `clearml-session` The entire content of the directory / file will be copied into your remote `clearml-session`
container under the `~/session-files/` directory. container under the `~/session-files/` directory.
Can be used in conjunction with `--store-session` to easily move workloads between local development machine Can be used in conjunction with `--store-workspace` to easily move workloads between local development machines
and remote machines with 100% persistent workspace synchronization. and remote machines with 100% persistent workspace synchronization.
```bash ```bash
@ -254,7 +254,7 @@ Click on the ID button next to the Task name, and copy the unique ID, then execu
clearml-session --debugging-session <experiment_id_here> clearml-session --debugging-session <experiment_id_here>
``` ```
Click on the JupyterLab/VSCode link, or drop directly into an SSH shell by typying `shell` Click on the JupyterLab/VSCode link, or drop directly into an SSH shell by typing `shell`.
## ❓ Frequently Asked Questions ## ❓ Frequently Asked Questions
@ -269,7 +269,7 @@ The local `clearml-session` awaits for the interactive Task to finish with the i
it connects via SSH to the host machine (see "safe and stable" above), and tunnels it connects via SSH to the host machine (see "safe and stable" above), and tunnels
both SSH and JupyterLab over the SSH connection. both SSH and JupyterLab over the SSH connection.
The end results is a local link which you can use to access the JupyterLab/VSCode on the remote machine, over a **secure and encrypted** connection! The end result is a local link which you can use to access the JupyterLab/VSCode on the remote machine, over a **secure and encrypted** connection!
#### Does `clearml-session` support Kubernetes clusters? #### Does `clearml-session` support Kubernetes clusters?
@ -277,7 +277,7 @@ Yes! `clearml-session` utilizes the `clearml-agent` kubernetes glue together wit
any clearml-session to spin a container (pod) on the kubernetes cluster and securely connect **directly** into the pod. any clearml-session to spin a container (pod) on the kubernetes cluster and securely connect **directly** into the pod.
This feature does not require any kubernetes access from the users, and simplifies code This feature does not require any kubernetes access from the users, and simplifies code
development on kubernetes clusters as well as job scheduling & launching. development on kubernetes clusters as well as job scheduling & launching.
Read more on how to deploy clearml on kubernetes [here](https://clear.ml/docs/latest/docs/clearml_agent/#kubernetes) Read more on how to deploy clearml on kubernetes [here](https://clear.ml/docs/latest/docs/clearml_agent/#kubernetes).
#### How can I use `clearml-session` to scale up / out development resources? #### How can I use `clearml-session` to scale up / out development resources?
@ -288,14 +288,14 @@ There is also a default docker image to use when initiating a task.
This means that using **clearml-session**s This means that using **clearml-session**s
with the autoscaler enabled, allows for turn-key secure development environment inside a docker of your choosing. with the autoscaler enabled, allows for turn-key secure development environment inside a docker of your choosing.
Learn more about it [here](https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler) & [here](https://clear.ml/docs/latest/docs/webapp/applications/apps_gpu_compute) Learn more about it [here](https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler) & [here](https://clear.ml/docs/latest/docs/webapp/applications/apps_gpu_compute).
#### Does `clearml-session` fit Work-From-Home setup? #### Does `clearml-session` fit Work-From-Home setup?
**YES**. Install `clearml-agent` on target machines inside the organization, connect over your company VPN **YES**. Install `clearml-agent` on target machines inside the organization, connect over your company VPN
and use `clearml-session` to gain access to a dedicated on-prem machine with the docker of your choosing and use `clearml-session` to gain access to a dedicated on-prem machine with the docker of your choosing
(with out-of-the-box support for any internal docker artifactory). (with out-of-the-box support for any internal docker artifactory).
Learn more about how to utilize your office workstations and on-prem machines [here](https://clear.ml/docs/latest/docs/clearml_agent) Learn more about how to utilize your office workstations and on-prem machines [here](https://clear.ml/docs/latest/docs/clearml_agent).
## ⌨️ CLI options ## ⌨️ CLI options