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>] [-f]
[--config <path>] [--remote <name>]
[--remote-config [<name>=<value> ...]]
url path
positional arguments:
url Location of DVC or Git repository to download from
path Path to a file or directory within the repositoryDescription
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 listfor 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.dvcfile). 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 dvc remote default containing the target for this to
work. The only exception is for local repos, where DVC will try to copy the data
from its cache first.
See
dvc get-urlto 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 apathto 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 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 is4 * cpu_count(). Using more jobs may speed up the operation. Note that the default value can be set in the source repo using thejobsconfig option ofdvc remote modify. -
-f,--force- when using--outto specify a local target file or directory, the operation will fail if those paths already exist. this flag will force the operation causing local files/dirs to be overwritten by the command. -
--show-url- instead of downloading the file or directory, just print the storage location (URL) of the target data. Ifpathis a Git-tracked file, this option is ignored. -
--config <path>- path to a config file that will be merged with the config in the target repository. -
--remote <name>- name of thedvc remoteto set as a default in the target repository. -
--remote-config [<name>=<value> ...]-dvc remoteconfig options to merge with a remote's config (default or one specified by--remote) in the target repository. -
-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 getcan 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.pklNote 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 will then
dvc pull the file from the dvc remote default 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 an 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 by 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.shExample: 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/d307aa005d6974a8525550956d5fb3remote.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-startedIf 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 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.pklNotice that the
urlprovided todvc getabove is..dvc getaccepts 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.pklAnd 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.pklExample: Set AWS profile for default remote
$ dvc get https://github.com/iterative/example-get-started-s3 data/prepared --remote-config profile=myprofileExample: Set default remote
$ dvc get https://github.com/iterative/example-get-started-s3 data/prepared --remote myremote