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get

Download a file or directory tracked by DVC or by Git into the current working directory.

See also our dvc.api.open() Python API function.

Synopsis

usage: dvc get [-h] [-q | -v] [-o <path>] [--rev <commit>]
               [--show-url] [-j <number>] url path

positional arguments:
  url              Location of DVC or Git repository to download from
  path             Path to a file or directory within the repository

Description

Provides an easy way to download files or directories tracked in any DVC repository (e.g. datasets, intermediate results, ML models), or Git repository (e.g. source code, small image/other files). dvc get copies the target file or directory (found at path in url) to the current working directory. (Analogous to wget, but for repos.)

See dvc list for a way to browse repository contents to find files or directories to download.

Note that unlike dvc import, this command does not track the downloaded files (does not create a .dvc file). For that reason, it doesn't require an existing DVC project to run in.

The url argument specifies the address of the DVC or Git repository containing the data source. Both HTTP and SSH protocols are supported (e.g. [user@]server:project.git). url can also be a local file system path (including the current project e.g. .).

The path argument specifies a file or directory to download (paths inside tracked directories are supported). It should be relative to the root of the repo (absolute paths are supported when url is local). Note that DVC-tracked targets must be found in a dvc.yaml or .dvc file of the repo.

⚠️ DVC repos should have a default DVC remote containing the target actual for this command to work. The only exception is for local repos, where DVC will try to copy the data from its cache first.

See dvc get-url to download data from other supported locations such as S3, SSH, HTTP, etc.

After running this command successfully, the data found in the url, path combination is created in the current working directory, with its original file name.

Options

  • -o <path>, --out <path> - specify a path to the desired location in the workspace to place the downloaded file or directory (instead of using the current working directory). Directories specified in the path will be created by this command.

  • --rev <commit> - commit hash, branch or tag name, etc. (any Git revision) of the repository to download the file or directory from. The latest commit in master (tip of the default branch) is used by default when this option is not specified.

  • -j <number>, --jobs <number> - parallelism level for DVC to download data from the remote. The default value is 4 * cpu_count(). Using more jobs may speed up the operation. Note that the default value can be set in the source repo using the jobs config option of dvc remote modify.

  • --show-url - instead of downloading the file or directory, just print the storage location (URL) of the target data. If path is a Git-tracked file, this option is ignored.

  • -h, --help - prints the usage/help message, and exit.

  • -q, --quiet - do not write anything to standard output. Exit with 0 if no problems arise, otherwise 1.

  • -v, --verbose - displays detailed tracing information.

Example: Get a DVC-tracked model

Note that dvc get can be used from anywhere in the file system, as long as DVC is installed.

We can use dvc get to download the resulting model file from our get started example repo, a DVC project hosted on GitHub:

$ dvc get https://github.com/iterative/example-get-started model.pkl
$ ls
model.pkl

Note that the model.pkl file doesn't actually exist in the root directory of the source Git repo. Instead, it's exported in the dvc.yaml file as an output of the train stage (in the outs field). DVC then pulls the file from the default remote of the source DVC project (found in its config file).

A recommended use for downloading binary files from DVC repositories, as done in this example, is to place a ML model inside a wrapper application that serves as an ETL pipeline or as an HTTP/RESTful API (web service) that provides predictions upon request. This can be automated leveraging DVC with CI/CD tools.

The same example applies to raw data or intermediate artifacts as well.

Examples: Get a misc. Git-tracked file

We can also use dvc get to retrieve any file or directory that exists in a Git repository.

$ dvc get https://github.com/schacon/cowsay install.sh
$ ls
install.sh

Example: Getting the storage URL of a DVC-tracked file

We can use dvc get --show-url to get the actual location where the final model file from our get started example repo is stored:

$ dvc get --show-url \
          https://github.com/iterative/example-get-started model.pkl
https://remote.dvc.org/get-started/c8/d307aa005d6974a8525550956d5fb3

remote.dvc.org/get-started is an HTTP DVC remote, whereas c8d307... is the file hash.

Example: Compare different versions of data or model

dvc get provides the --rev option to specify which Git commit of the repository to download the file or directory from. It also has the --out option to specify the location to place the target data within the workspace. Combining these two options allows us to do something we can't achieve with the regular git checkout + dvc checkout process – see for example the Switching between versions chapter of our Get Started.

Let's use the get started example repo again, like in the previous example. But this time, clone it first to see dvc get in action inside a DVC project.

$ git clone https://github.com/iterative/example-get-started
$ cd example-get-started

If you are familiar with the project in our Get Started (used in these examples), you may remember that the chapter where we train a first version of the model corresponds to the the baseline-experiment tag in the repo. Similarly bigrams-experiment points to an improved model (trained using bigrams). What if we wanted to have both versions of the model "checked out" at the same time? dvc get provides an easy way to do this:

$ dvc get . model.pkl --rev baseline-experiment \
                      --out model.monograms.pkl

Notice that the url provided to dvc get above is .. dvc get accepts file system paths as the "URL" to the source repo, for edge cases.

The model.monograms.pkl file now contains the older version of the model. To get the most recent one, we use a similar command, but with -o model.bigrams.pkl and --rev bigrams-experiment (or even without --rev since that tag has the latest model version anyway). In fact, in this case using dvc pull with the corresponding stage as target should suffice, downloading the file as just model.pkl. We can then rename it to make its variant explicit:

$ dvc pull train
$ mv model.pkl model.bigrams.pkl

And that's it! Now we have both model files in the workspace, with different names, and not currently tracked by Git:

$ git status
...
Untracked files:
  (use "git add <file> ..." to include in what will be committed)

	model.bigrams.pkl
	model.monograms.pkl