All-in-one web-based development environment for machine learning

Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issues • Contribution

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated.

Highlights

• 💫 Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.
• 🗃 Pre-installed with many popular data science libraries & tools.
• 🖥 Full Linux desktop GUI accessible via web browser.
• 🔀 Seamless Git integration optimized for notebooks.
• 📈 Integrated hardware & training monitoring via Tensorboard & Netdata.
• 🚪 Access from anywhere via Web, SSH, or VNC under a single port.
• 🎛 Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH.
• 🐳 Easy to deploy on Mac, Linux, and Windows via Docker.

Getting Started

Prerequisites

The workspace requires Docker to be installed on your machine ( 📖 Installation Guide).

Start single instance

Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 drasey/ml-workspace:latest

Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet speed. Once the workspace is started, you can access it via http://localhost:8080.

If started on another machine or with a different port, make sure to use the machine's IP/DNS and/or the exposed port.

To deploy a single instance for productive usage, we recommend to apply at least the following options:

docker run -d \
    -p 8080:8080 \
    --name "ml-workspace" \
    -v "${PWD}:/workspace" \
    --env AUTHENTICATE_VIA_JUPYTER="mytoken" \
    --shm-size 512m \
    --restart always \
    drasey/ml-workspace:latest

This command runs the container in background (-d), mounts your current working directory into the /workspace folder (-v), secures the workspace via a provided token (--env AUTHENTICATE_VIA_JUPYTER), provides 512MB of shared memory (--shm-size) to prevent unexpected crashes (see known issues section), and keeps the container running even on system restarts (--restart always). You can find additional options for docker run here and workspace configuration options in the section below.

Configuration Options

The workspace provides a variety of configuration options that can be used by setting environment variables (via docker run option: --env).

Persist Data

To persist the data, you need to mount a volume into /workspace (via docker run option: -v).

Enable Authentication

We strongly recommend enabling authentication via one of the following two options. For both options, the user will be required to authenticate for accessing any of the pre-installed tools.

docker run -p 8080:8080 --env AUTHENTICATE_VIA_JUPYTER="mytoken" drasey/ml-workspace:latest

docker run -p 8080:8080 --env WORKSPACE_AUTH_USER="user" --env WORKSPACE_AUTH_PASSWORD="pwd" drasey/ml-workspace:latest

Enable SSL/HTTPS

We recommend enabling SSL so that the workspace is accessible via HTTPS (encrypted communication). SSL encryption can be activated via the WORKSPACE_SSL_ENABLED variable.

docker run \
    -p 8080:8080 \
    --env WORKSPACE_SSL_ENABLED="true" \
    -v /path/with/certificate/files:/resources/ssl:ro \
    drasey/ml-workspace:latest

Limit Memory & CPU

By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. Docker provides ways to control how much memory, or CPU a container can use, by setting runtime configuration flags of the docker run command.

The workspace requires atleast 2 CPUs and 500MB to run stable and be usable.

docker run -p 8080:8080 --cpus=8 --memory=16g --shm-size=1G drasey/ml-workspace:latest

Enable Proxy

If a proxy is required, you can pass the proxy configuration via the HTTP_PROXY, HTTPS_PROXY, and NO_PROXY environment variables.

Multi-user setup

The workspace is designed as a single-user development environment. For a multi-user setup, we recommend deploying 🧰 ML Hub. ML Hub is based on JupyterHub with the task to spawn, manage, and proxy workspace instances for multiple users.

docker run -p 8080:8080 -v /var/run/docker.sock:/var/run/docker.sock drasey/ml-hub:latest

Features

Jupyter • Desktop GUI • VS Code • JupyterLab • Git Integration • File Sharing • Access Ports • Tensorboard • Extensibility • Hardware Monitoring • SSH Access • Remote Development • Job Execution

The workspace is equipped with a selection of best-in-class open-source development tools to help with the machine learning workflow. Many of these tools can be started from the Open Tool menu from Jupyter (the main application of the workspace):

Within your workspace you have full root & sudo privileges to install any library or tool you need via terminal (e.g., pip, apt-get, conda, or npm). You can find more ways to extend the workspace within the Extensibility section

Jupyter

Jupyter Notebook is a web-based interactive environment for writing and running code. The main building blocks of Jupyter are the file-browser, the notebook editor, and kernels. The file-browser provides an interactive file manager for all notebooks, files, and folders in the /workspace directory.

A new notebook can be created by clicking on the New drop-down button at the top of the list and selecting the desired language kernel.

You can spawn interactive terminal instances as well by selecting New -> Terminal in the file-browser.

The notebook editor enables users to author documents that include live code, markdown text, shell commands, LaTeX equations, interactive widgets, plots, and images. These notebook documents provide a complete and self-contained record of a computation that can be converted to various formats and shared with others.

This workspace has a variety of third-party Jupyter extensions activated. You can configure these extensions in the nbextensions configurator: nbextensions tab on the file browser

The Notebook allows code to be run in a range of different programming languages. For each notebook document that a user opens, the web application starts a kernel that runs the code for that notebook and returns output. This workspace has a Python 3 kernel pre-installed. Additional Kernels can be installed to get access to other languages (e.g., R, Scala, Go) or additional computing resources (e.g., GPUs, CPUs, Memory).

Python 2 is deprected and we do not recommend to use it. However, you can still install a Python 2.7 kernel via this command: /bin/bash /resources/tools/python-27.sh

Clipboard: If you want to share the clipboard between your machine and the workspace, you can use the copy-paste functionality as described below:

💡 Long-running tasks: Use the desktop GUI for long-running Jupyter executions. By running notebooks from the browser of your workspace desktop GUI, all output will be synchronized to the notebook even if you have disconnected your browser from the notebook.

Visual Studio Code

Visual Studio Code (Open Tool -> VS Code) is an open-source lightweight but powerful code editor with built-in support for a variety of languages and a rich ecosystem of extensions. It combines the simplicity of a source code editor with powerful developer tooling, like IntelliSense code completion and debugging. The workspace integrates VS Code as a web-based application accessible through the browser-based on the awesome code-server project. It allows you to customize every feature to your liking and install any number of third-party extensions.

The workspace also provides a VS Code integration into Jupyter allowing you to open a VS Code instance for any selected folder, as shown below:

JupyterLab

JupyterLab (Open Tool -> JupyterLab) is the next-generation user interface for Project Jupyter. It offers all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. This JupyterLab instance comes pre-installed with a few helpful extensions such as a the jupyterlab-toc, jupyterlab-git, and juptyterlab-tensorboard.

Clone Repository

For cloning repositories via https, we recommend to navigate to the desired root folder and to click on the git button as shown below:

This might ask for some required settings and, subsequently, opens ungit, a web-based Git client with a clean and intuitive UI that makes it convenient to sync your code artifacts. Within ungit, you can clone any repository. If authentication is required, you will get asked for your credentials.

Push, Pull, Merge, and Other Git Actions

To commit and push a single notebook to a remote Git repository, we recommend to use the Git plugin integrated into Jupyter, as shown below:

For more advanced Git operations, we recommend to use ungit. With ungit, you can do most of the common git actions such as push, pull, merge, branch, tag, checkout, and many more.

Diffing and Merging Notebooks

Jupyter notebooks are great, but they often are huge files, with a very specific JSON file format. To enable seamless diffing and merging via Git this workspace is pre-installed with nbdime. Nbdime understands the structure of notebook documents and, therefore, automatically makes intelligent decisions when diffing and merging notebooks. In the case you have merge conflicts, nbdime will make sure that the notebook is still readable by Jupyter, as shown below:

Furthermore, the workspace comes pre-installed with jupytext, a Jupyter plugin that reads and writes notebooks as plain text files. This allows you to open, edit, and run scripts or markdown files (e.g., .py, .md) as notebooks within Jupyter. In the following screenshot, we have opened a markdown file via Jupyter:

In combination with Git, jupytext enables a clear diff history and easy merging of version conflicts. With both of those tools, collaborating on Jupyter notebooks with Git becomes straightforward.

File Sharing

The workspace has a feature to share any file or folder with anyone via a token-protected link. To share data via a link, select any file or folder from the Jupyter directory tree and click on the share button as shown in the following screenshot:

This will generate a unique link protected via a token that gives anyone with the link access to view and download the selected data via the Filebrowser UI:

To deactivate or manage (e.g., provide edit permissions) shared links, open the Filebrowser via Open Tool -> Filebrowser and select Settings->User Management.

Remote Development

The workspace can be integrated and used as a remote runtime (also known as remote kernel/machine/interpreter) for a variety of popular development tools and IDEs, such as Jupyter, VS Code, PyCharm, Colab, or Atom Hydrogen. Thereby, you can connect your favorite development tool running on your local machine to a remote machine for code execution. This enables a local-quality development experience with remote-hosted compute resources.

These integrations usually require a passwordless SSH connection from the local machine to the workspace. To set up an SSH connection, please follow the steps explained in the SSH Access section.

Jupyter - Remote Kernel (click to expand...)

The workspace can be added to a Jupyter instance as a remote kernel by using the remote_ikernel tool. If you have installed remote_ikernel (pip install remote_ikernel) on your local machine, the SSH setup script of the workspace will automatically offer you the option to setup a remote kernel connection.

When running kernels on remote machines, the notebooks themselves will be saved onto the local filesystem, but the kernel will only have access to the filesystem of the remote machine running the kernel. If you need to sync data, you can make use of rsync, scp, or sshfs as explained in the SSH Access section.

In case you want to manually setup and manage remote kernels, use the remote_ikernel command-line tool, as shown below:

# Change my-workspace with the name of a workspace SSH connection
remote_ikernel manage --add \
    --interface=ssh \
    --kernel_cmd="ipython kernel -f {connection_file}" \
    --name="ml-server Py 3.6" \
    --host="my-workspace"

You can use the remote_ikernel command line functionality to list (remote_ikernel manage --show) or delete (remote_ikernel manage --delete ) remote kernel connections.

VS Code - Remote Machine (click to expand...)

The Visual Studio Code Remote - SSH extension allows you to open a remote folder on any remote machine with SSH access and work with it just as you would if the folder were on your own machine. Once connected to a remote machine, you can interact with files and folders anywhere on the remote filesystem and take full advantage of VS Code's feature set (IntelliSense, debugging, and extension support). The discovers and works out-of-the-box with passwordless SSH connections as configured by the workspace SSH setup script. To enable your local VS Code application to connect to a workspace:

1. Install Remote - SSH extension inside your local VS Code.
2. Run the SSH setup script of a selected workspace as explained in the SSH Access section.
3. Open the Remote-SSH panel in your local VS Code. All configured SSH connections should be automatically discovered. Just select any configured workspace connection you like to connect to as shown below:

📖 You can find additional features and information about the Remote SSH extension in this guide.

Tensorboard

Tensorboard provides a suite of visualization tools to make it easier to understand, debug, and optimize your experiment runs. It includes logging features for scalar, histogram, model structure, embeddings, and text & image visualization. The workspace comes pre-installed with jupyter_tensorboard extension that integrates Tensorboard into the Jupyter interface with functionalities to start, manage, and stop instances. You can open a new instance for a valid logs directory, as shown below:

If you have opened a Tensorboard instance in a valid log directory, you will see the visualizations of your logged data:

Tensorboard can be used in combination with many other ML frameworks besides Tensorflow. By using the tensorboardX library you can log basically from any python based library. Also, PyTorch has a direct Tensorboard integration as described here.

If you prefer to see the tensorboard directly within your notebook, you can make use of following Jupyter magic:

%load_ext tensorboard.notebook
%tensorboard --logdir /workspace/path/to/logs

Run as a job

A job is defined as any computational task that runs for a certain time to completion, such as a model training or a data pipeline.

The workspace image can also be used to execute arbitrary Python code without starting any of the pre-installed tools. This provides a seamless way to productize your ML projects since the code that has been developed interactively within the workspace will have the same environment and configuration when run as a job via the same workspace image.

docker run --env EXECUTE_CODE="git+https://github.com/ml-tooling/ml-workspace.git#subdirectory=resources/tests/ml-job" drasey/ml-workspace:latest

docker run -v "${PWD}:/workspace/ml-job/" --env EXECUTE_CODE="/workspace/ml-job/" drasey/ml-workspace:latest

python /resources/scripts/execute_code.py /path/to/your/job

FROM drasey/ml-workspace:latest

# Add job code to image
COPY ml-job /workspace/ml-job
ENV EXECUTE_CODE=/workspace/ml-job

# Install requirements only
RUN python /resources/scripts/execute_code.py --requirements-only

# Execute only the code at container startup
CMD ["python", "/resources/docker-entrypoint.py", "--code-only"]

Pre-installed Libraries and Interpreters

The workspace is pre-installed with many popular interpreters, data science libraries, and ubuntu packages:

• Interpreter: Python 3.7 (Miniconda 3), Java 11 (OpenJDK), NodeJS 13, Scala, Perl 5
• Python libraries: Tensorflow, Keras, Pytorch, Sklearn, XGBoost, MXNet, Theano, and many more
• Package Manager: conda, pip, npm, apt-get, yarn, sdk, gdebi, mvn ...

The full list of installed tools can be found within the Dockerfile.

For every minor version release, we run vulnerability, virus, and security checks within the workspace using vuls, safety, and clamav to make sure that the workspace environment is as secure as possible.

Extensibility

The workspace provides a high degree of extensibility. Within the workspace, you have full root & sudo privileges to install any library or tool you need via terminal (e.g., pip, apt-get, conda, or npm). You can open a terminal by one of the following ways:

• Jupyter: New -> Terminal
• Desktop VNC: Applications -> Terminal Emulator
• JupyterLab: File -> New -> Terminal
• VS Code: Terminal -> New Terminal

Additionally, pre-installed tools such as Jupyter, JupyterLab, and Visual Studio Code each provide their own rich ecosystem of extensions. The workspace also contains a collection of installer scripts for many commonly used development tools or libraries (e.g., PyCharm, Zeppelin, RStudio, Starspace). You can find and execute all tool installers via Open Tool -> Install Tool. Those scripts can be also executed from the Desktop VNC (double-click on the script within the Tools folder on the Desktop VNC).

/resources/tools/zeppelin.sh --port=1234

As an alternative to extending the workspace at runtime, you can also customize the workspace Docker image to create your own flavor as explained in the FAQ section.

FAQ

# Extend from any of the workspace versions/flavors
# Using latest as version is not recommended, please specify a specific version
FROM drasey/ml-workspace:latest

# Run you customizations, e.g.
RUN \
    # Install r-runtime, r-kernel, and r-studio web server from provided install scripts
    /bin/bash $RESOURCES_PATH/tools/r-runtime.sh --install && \
    /bin/bash $RESOURCES_PATH/tools/r-studio-server.sh --install && \
    # Cleanup Layer - removes unneccessary cache files
    clean-layer.sh

docker run -d \
    -p 8080:8080 \
    --name "ml-workspace" \
    -v "/path/on/host:/workspace" \
    --env AUTHENTICATE_VIA_JUPYTER="mytoken" \
    --restart always \
    drasey/ml-workspace:0.8.7

Known Issues

docker run --shm-size=2G drasey/ml-workspace:latest

import os
MAX_NUM_THREADS = int(os.getenv("MAX_NUM_THREADS"))

# Set in pytorch
import torch
torch.set_num_threads(MAX_NUM_THREADS)

# Set in tensorflow
import tensorflow as tf
config = tf.ConfigProto(
    device_count={"CPU": MAX_NUM_THREADS},
    inter_op_parallelism_threads=MAX_NUM_THREADS,
    intra_op_parallelism_threads=MAX_NUM_THREADS,
)
tf_session = tf.Session(config=config)

# Set session for keras
import keras.backend as K
K.set_session(tf_session)

# Set in sklearn estimator
from sklearn.linear_model import LogisticRegression
LogisticRegression(n_jobs=MAX_NUM_THREADS).fit(X, y)

# Set for multiprocessing pool
from multiprocessing import Pool

with Pool(MAX_NUM_THREADS) as pool:
    results = pool.map(lst)

exited: nginx (terminated by SIGILL (core dumped); not expected)

Build

Execute this command in the project root folder to build the docker container:

python build.py --version={MAJOR.MINOR.PATCH-TAG}

The version is optional and should follow the Semantic Versioning standard (MAJOR.MINOR.PATCH). For additional script options:

python build.py --help

Deploy

Execute this command in the project root folder to push the container to the configured docker registry:

python build.py --deploy --version={MAJOR.MINOR.PATCH-TAG}

The version has to be provided. The version format should follow the Semantic Versioning standard (MAJOR.MINOR.PATCH). For additional script options:

python build.py --help

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