[{"_1":2,"_182":-5,"_183":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_161":162},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_89":90,"_112":113,"_150":-5,"_151":11,"_152":21,"_153":11,"_154":-5,"_155":11,"_156":-5,"_157":158,"_159":114,"_160":-7},"canonicalUrl","https://hub.docker.com/r/dagshub/ml-workspace/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","dagshub","reponame","ml-workspace","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":69,"_70":71,"_72":11,"_73":33,"_74":75,"_80":11,"_81":82,"_86":33,"_87":88},"categories",[],"content_types",[44],"image","date_registered","2020-08-26T11:48:44.135241Z","description","Fork of https://github.com/ml-tooling/ml-workspace which is up to date","is_private","last_updated","2020-09-02T12:05:51.114135Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",14484,"repository_type","star_count",5,"status",1,"status_description","active","source","collaborator_count",0,"full_description","# This Repository is a fork that is being maintained. Feel free to use it, create PR and requests if you have any.\n\n

\n \n $\"\"$ \n
\n

\n\n

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

\n\n

\n \n \n \n \n \n \n

\n\n

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

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

\n $\"Try$ \n

\n\n### Prerequisites\n\nThe workspace requires **Docker** to be installed on your machine ([📖 Installation Guide](https://docs.docker.com/install/#supported-platforms)).\n\n### Start single instance\n\nDeploying a single workspace instance is as simple as:\n\n```bash\ndocker run -p 8080:8080 dagshub/ml-workspace:latest\n```\n\nVoilà, 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.\n\n> _If started on another machine or with a different port, make sure to use the machine's IP/DNS and/or the exposed port._\n\nTo deploy a single instance for productive usage, we recommend to apply at least the following options:\n\n```bash\ndocker run -d \\\n -p 8080:8080 \\\n --name \"ml-workspace\" -v \"${PWD}:/workspace\" \\\n --env AUTHENTICATE_VIA_JUPYTER=\"mytoken\" \\\n --shm-size 512m \\\n --restart always \\\n dagshub/ml-workspace:latest\n```\n\nThis 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](#known-issues)), and keeps the container running even on system restarts (`--restart always`). You can find additional options for docker run [here](https://docs.docker.com/engine/reference/commandline/run/) and workspace configuration options in [the section below](#Configuration).\n\n### Configuration Options\n\nThe workspace provides a variety of configuration options that can be used by setting environment variables (via docker run option: `--env`).\n\n

Configuration options (click to expand...)

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Variable	Description	Default
WORKSPACE_BASE_URL	The base URL under which Jupyter and all other tools will be reachable from.	/
WORKSPACE_SSL_ENABLED	Enable or disable SSL. When set to true, either certificate (cert.crt) must be mounted to `/resources/ssl` or, if not, the container generates self-signed certificate.	false
WORKSPACE_AUTH_USER	Basic auth user name. To enable basic auth, both the user and password need to be set. We recommend to use the `AUTHENTICATE_VIA_JUPYTER` for securing the workspace.
WORKSPACE_AUTH_PASSWORD	Basic auth user password. To enable basic auth, both the user and password need to be set. We recommend to use the `AUTHENTICATE_VIA_JUPYTER` for securing the workspace.
WORKSPACE_PORT	Configures the main container-internal port of the workspace proxy. For most scenarios, this configuration should not be changed, and the port configuration via Docker should be used instead of the workspace should be accessible from a different port.	8080
CONFIG_BACKUP_ENABLED	Automatically backup and restore user configuration to the persisted `/workspace` folder, such as the .ssh, .jupyter, or .gitconfig from the users home directory.	true
SHARED_LINKS_ENABLED	Enable or disable the capability to share resources via external links. This is used to enable file sharing, access to workspace-internal ports, and easy command-based SSH setup. All shared links are protected via a token. However, there are certain risks since the token cannot be easily invalidated after sharing and does not expire.	true
INCLUDE_TUTORIALS	If `true`, a selection of tutorial and introduction notebooks are added to the `/workspace` folder at container startup, but only in if the folder is empty.	true
MAX_NUM_THREADS	The number of threads used for computations when using various common libraries (MKL, OPENBLAS, OMP, NUMBA, ...). You can also use `auto` to let the workspace dynamically determine the number of threads based on available CPU resources. This configuration can be overwritten by the user from within the workspace. Generally, it is good to set it at or below the number of CPUs available to the workspace.	auto
Jupyter Configuration:
SHUTDOWN_INACTIVE_KERNELS	Automatically shutdown inactive kernels after a given timeout (to clean up memory or GPU resources). Value can be either a timeout in seconds or set to `true` with a default value of 48h.	false
AUTHENTICATE_VIA_JUPYTER	If `true`, all HTTP requests will be authenticated against the Jupyter server, meaning that the authentication method configured with Jupyter will be used for all other tools as well. This can be deactivated with `false`. Any other value will activate this authentication and are applied as token via NotebookApp.token configuration of Jupyter.	false
NOTEBOOK_ARGS	Add and overwrite Jupyter configuration options via command line args. Refer to this overview for all options.

\n\n

\n\n### Persist Data\n\nTo persist the data, you need to mount a volume into `/workspace` (via docker run option: `-v`).\n\n

Details (click to expand...)

\n\nThe default work directory within the container is `/workspace`, which is also the root directory of the Jupyter instance. The `/workspace` directory is intended to be used for all the important work artifacts. Data within other directories of the server (e.g., `/root`) might get lost at container restarts.\n

\n\n### Enable Authentication\n\nWe 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.\n\n

Details (click to expand...)

\n\n#### Token-based Authentication via Jupyter (recommended)\n\nActivate the token-based authentication based on the authentication implementation of Jupyter via the `AUTHENTICATE_VIA_JUPYTER` variable:\n\n```bash\ndocker run -p 8080:8080 --env AUTHENTICATE_VIA_JUPYTER=\"mytoken\" dagshub/ml-workspace:latest\n```\n\nYou can also use `` to let Jupyter generate a random token that is printed out on the container logs. A value of `true` will not set any token but activate that every request to any tool in the workspace will be checked with the Jupyter instance if the user is authenticated. This is used for tools like JupyterHub, which configures its own way of authentication.\n\n#### Basic Authentication via Nginx\n\nActivate the basic authentication via the `WORKSPACE_AUTH_USER` and `WORKSPACE_AUTH_PASSWORD` variable:\n\n```bash\ndocker run -p 8080:8080 --env WORKSPACE_AUTH_USER=\"user\" --env WORKSPACE_AUTH_PASSWORD=\"pwd\" dagshub/ml-workspace:latest\n```\n\nThe basic authentication is configured via the nginx proxy and might be more performant compared to the other option since with `AUTHENTICATE_VIA_JUPYTER` every request to any tool in the workspace will check via the Jupyter instance if the user (based on the request cookies) is authenticated.\n\n

\n\n### Enable SSL/HTTPS\n\nWe recommend enabling SSL so that the workspace is accessible via HTTPS (encrypted communication). SSL encryption can be activated via the `WORKSPACE_SSL_ENABLED` variable. \n\n

Details (click to expand...)

\n\nWhen set to `true`, either the `cert.crt` and `cert.key` file must be mounted to `/resources/ssl` or, if the certificate files do not exist, the container generates self-signed certificates. For example, if the `/path/with/certificate/files` on the local system contains a valid certificate for the host domain (`cert.crt` and `cert.key` file), it can be used from the workspace as shown below:\n\n```bash\ndocker run \\\n -p 8080:8080 \\\n --env WORKSPACE_SSL_ENABLED=\"true\" \\\n -v /path/with/certificate/files:/resources/ssl:ro \\\n dagshub/ml-workspace:latest\n```\n\nIf you want to host the workspace on a public domain, we recommend to use [Let's encrypt](https://letsencrypt.org/getting-started/) to get a trusted certificate for your domain. To use the generated certificate (e.g., via [certbot](https://certbot.eff.org/) tool) for the workspace, the `privkey.pem` corresponds to the `cert.key` file and the `fullchain.pem` to the `cert.crt` file.\n\n> _When you enable SSL support, you must access the workspace over `https://`, not over plain `http://`._\n\n

\n\n### Limit Memory & CPU\n\nBy 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.\n\n> _The workspace requires atleast 2 CPUs and 500MB to run stable and be usable._\n\n

Details (click to expand...)

\n\nFor example, the following command restricts the workspace to only use a maximum of 8 CPUs, 16 GB of memory, and 1 GB of shared memory (see [Known Issues](#known-issues)):\n\n```bash\ndocker run -p 8080:8080 --cpus=8 --memory=16g --shm-size=1G dagshub/ml-workspace:latest\n```\n\n> 📖 _For more options and documentation on resource constraints, please refer to the [official docker guide](https://docs.docker.com/config/containers/resource_constraints/)._\n\n

\n\n### Enable Proxy\n\nIf a proxy is required, you can pass the proxy configuration via the `HTTP_PROXY`, `HTTPS_PROXY`, and `NO_PROXY` environment variables.\n\n### Workspace Flavors\n\nIn addition to the main workspace image (`dagshub/ml-workspace`), we provide other image flavors that extend the features or minimize the image size to support a variety of use cases.\n\n#### Minimal Flavor\n\n

\n\n\n\n\n

\n\n

Details (click to expand...)

\n\nThe minimal flavor (`dagshub/ml-workspace-minimal`) is our smallest image that contains most of the tools and features described in the [features section](#features) without most of the python libraries that are pre-installed in our main image. Any Python library or excluded tool can be installed manually during runtime by the user.\n\n```bash\ndocker run -p 8080:8080 dagshub/ml-workspace-minimal:latest\n```\n

\n\n#### R Flavor\n\n

\n\n\n\n\n

\n\n

Details (click to expand...)

\n\nThe R flavor (`dagshub/ml-workspace-r`) is based on our default workspace image and extends it with the R-interpreter, R-Jupyter kernel, RStudio server (access via `Open Tool -> RStudio`), and a variety of popular packages from the R ecosystem.\n\n```bash\ndocker run -p 8080:8080 dagshub/ml-workspace-r:latest\n```\n

\n\n#### Spark Flavor\n\n

\n\n\n\n\n

\n\n

Details (click to expand...)

\n\nThe Spark flavor (`dagshub/ml-workspace-spark`) is based on our R-flavor workspace image and extends it with the Spark-interpreter, Spark-Jupyter kernel (Apache Toree), Zeppelin Notebook (access via `Open Tool -> Zeppelin`), and a few additional python libraries & Jupyter extensions.\n\n```bash\ndocker run -p 8080:8080 dagshub/ml-workspace-spark:latest\n```\n\n

\n\n#### GPU Flavor\n\n

\n\n\n\n\n

\n\n

Details (click to expand...)

\n\n> _Currently, the GPU-flavor only supports CUDA 10.1. Support for other CUDA versions might be added in the future._\n\nThe GPU flavor (`dagshub/ml-workspace-gpu`) is based on our default workspace image and extends it with CUDA 10.1 and GPU-ready versions of various machine learning libraries (e.g., tensorflow, pytorch, cntk, jax). This GPU image has the following additional requirements for the system:\n\n- Nvidia Drivers for the GPUs. Drivers need to be CUDA 10.1 compatible, version `>= 418.39` ([📖 Instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver)).\n- (Docker >= 19.03) Nvidia Container Toolkit ([📖 Instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(Native-GPU-Support))).\n\n```bash\ndocker run -p 8080:8080 --gpus all dagshub/ml-workspace-gpu:latest\n```\n\n- (Docker < 19.03) Nvidia Docker 2.0 ([📖 Instructions](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0))).\n\n```bash\ndocker run -p 8080:8080 --runtime nvidia --env NVIDIA_VISIBLE_DEVICES=\"all\" dagshub/ml-workspace-gpu:latest\n```\n\nThe GPU flavor also comes with a few additional configuration options, as explained below:\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Variable	Description	Default
NVIDIA_VISIBLE_DEVICES	Controls which GPUs will be accessible inside the workspace. By default, all GPUs from the host are accessible within the workspace. You can either use `all`, `none`, or specify a comma-separated list of device IDs (e.g., `0,1`). You can find out the list of available device IDs by running `nvidia-smi` on the host machine.	all
CUDA_VISIBLE_DEVICES	Controls which GPUs CUDA applications running inside the workspace will see. By default, all GPUs that the workspace has access to will be visible. To restrict applications, provide a comma-separated list of internal device IDs (e.g., `0,2`) based on the available devices within the workspace (run `nvidia-smi`). In comparison to `NVIDIA_VISIBLE_DEVICES`, the workspace user will be still able to access other GPUs by overwriting this configuration from within the workspace.
TF_FORCE_GPU_ALLOW_GROWTH	By default, the majority of GPU memory will be allocated by the first execution of a TensorFlow graph. While this behavior can be desirable for production pipelines, it is less desirable for interactive use. Use `true` to enable dynamic GPU Memory allocation or `false` to instruct TensorFlow to allocate all memory at execution.	true

\n
\n\n## Support\nThis project seems to be no longer maintained by its original creators. It is now maintained here by DAGsHub.\nPlease understand that we won't be able to provide individual support via email. We also believe that help is much more\nvaluable if it's shared publicly so that more people can benefit from it.\n\n| Type | Channel |\n| ------------------------ | ------------------------------------------------------ |\n| 🚨 **Bug Reports** |

|\n| 🎁 **Feature Requests** |

|\n| 👩‍💻 **Usage Questions** |

|\n| 🗯 **General Discussion** |

|\n\n---\n\n
\n\n## Features\n\n

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

\n\nThe 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):\n\n

\n\n> _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](#extensibility) section_\n\n### Jupyter\n\n[Jupyter Notebook](https://jupyter.org/) 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.\n\n

\n\nA 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.\n\n> _You can spawn interactive **terminal** instances as well by selecting `New -> Terminal` in the file-browser._\n\n

\n\nThe notebook editor enables users to author documents that include live code, markdown text, shell commands, LaTeX equations, interactive widgets, plots, and images. These ","has_starred","hub_user","immutable_tags_settings",{"_76":11,"_77":78},"enabled","rules",[79],".*","is_automated","permissions",{"_83":21,"_84":11,"_85":11},"read","write","admin","user","storage_size",3233865439,"orgData",{"_91":92,"_93":94,"_95":96,"_97":33,"_98":99,"_100":101,"_102":99,"_103":104,"_105":106,"_107":108,"_109":108,"_110":108,"_111":21},"id","7f7ff237b76b4a6f83e512238b44a3a8","uuid","7f7ff237-b76b-4a6f-83e5-12238b44a3a8","type","organization","orgname","full_name","DAGsHub","location","San Francisco","company","profile_url","https://dagshub.com","date_joined","2020-08-26T11:42:50.3947Z","gravatar_url","","gravatar_email","badge","is_active","recentTags",[114],{"_115":44,"_116":117,"_118":119,"_91":120,"_121":122,"_50":139,"_140":117,"_141":33,"_142":56,"_57":143,"_144":145,"_146":130,"_147":139,"_148":66,"_149":21},"content_type","creator",10733783,"full_size",3233795047,114807049,"images",[123],{"_124":125,"_126":127,"_128":108,"_129":130,"_131":132,"_133":134,"_135":108,"_136":-5,"_137":119,"_63":66,"_138":-5},"architecture","amd64","digest","sha256:3424d656ef88061fc8f095a6d878c48dc0f2bf13b3e01fe4beb5000dcd8262cd","features","last_pulled","2026-10-04T12:42:41.638508915Z","last_pushed","2020-08-26T14:19:35Z","os","linux","os_features","os_version","size","variant","2020-09-02T12:05:50.741321Z","last_updater","last_updater_username","media_type","latest","repository",9708202,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"selectedTag","attestationsUrl","routes/_layout.r.$namespace.$reponame.tags",{"_38":163,"_170":171,"_157":180,"_181":11},{"_40":164,"_42":165,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":166,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":69,"_70":71,"_72":11,"_73":33,"_74":167,"_80":11,"_81":169,"_86":33,"_87":88},[],[44],[56],{"_76":11,"_77":168},[79],{"_83":21,"_84":11,"_85":11},"tags",{"_172":64,"_173":-5,"_174":-5,"_175":176},"count","next","previous","results",[177],{"_115":44,"_116":117,"_118":119,"_91":120,"_121":178,"_50":139,"_140":117,"_141":33,"_142":56,"_57":143,"_144":145,"_146":130,"_147":139,"_148":66,"_149":21},[179],{"_124":125,"_126":127,"_128":108,"_129":130,"_131":132,"_133":134,"_135":108,"_136":-5,"_137":119,"_63":66,"_138":-5},[],"canDeleteTags","actionData","errors"]

dagshub/ml-workspace

Tags for dagshub/ml-workspace