Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (12.4%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Containers for machine learning
Basic Info
- Host: GitHub
- Owner: replicate
- License: apache-2.0
- Language: Go
- Default Branch: main
- Homepage: https://cog.run
- Size: 28.7 MB
Statistics
- Stars: 8,820
- Watchers: 68
- Forks: 624
- Open Issues: 448
- Releases: 205
Topics
Metadata Files
README.md
Cog: Containers for machine learning
Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container.
You can deploy your packaged model to your own infrastructure, or to Replicate.
Highlights
📦 Docker containers without the pain. Writing your own
Dockerfilecan be a bewildering process. With Cog, you define your environment with a simple configuration file and it generates a Docker image with all the best practices: Nvidia base images, efficient caching of dependencies, installing specific Python versions, sensible environment variable defaults, and so on.🤬️ No more CUDA hell. Cog knows which CUDA/cuDNN/PyTorch/Tensorflow/Python combos are compatible and will set it all up correctly for you.
✅ Define the inputs and outputs for your model with standard Python. Then, Cog generates an OpenAPI schema and validates the inputs and outputs with Pydantic.
🎁 Automatic HTTP prediction server: Your model's types are used to dynamically generate a RESTful HTTP API using FastAPI.
🥞 Automatic queue worker. Long-running deep learning models or batch processing is best architected with a queue. Cog models do this out of the box. Redis is currently supported, with more in the pipeline.
☁️ Cloud storage. Files can be read and written directly to Amazon S3 and Google Cloud Storage. (Coming soon.)
🚀 Ready for production. Deploy your model anywhere that Docker images run. Your own infrastructure, or Replicate.
How it works
Define the Docker environment your model runs in with cog.yaml:
yaml
build:
gpu: true
system_packages:
- "libgl1-mesa-glx"
- "libglib2.0-0"
python_version: "3.12"
python_packages:
- "torch==2.3"
predict: "predict.py:Predictor"
Define how predictions are run on your model with predict.py:
```python from cog import BasePredictor, Input, Path import torch
class Predictor(BasePredictor): def setup(self): """Load the model into memory to make running multiple predictions efficient""" self.model = torch.load("./weights.pth")
# The arguments and types the model takes as input
def predict(self,
image: Path = Input(description="Grayscale input image")
) -> Path:
"""Run a single prediction on the model"""
processed_image = preprocess(image)
output = self.model(processed_image)
return postprocess(output)
```
In the above we accept a path to the image as an input, and return a path to our transformed image after running it through our model.
Now, you can run predictions on this model:
console
$ cog predict -i image=@input.jpg
--> Building Docker image...
--> Running Prediction...
--> Output written to output.jpg
Or, build a Docker image for deployment:
```console $ cog build -t my-colorization-model --> Building Docker image... --> Built my-colorization-model:latest
$ docker run -d -p 5000:5000 --gpus all my-colorization-model
$ curl http://localhost:5000/predictions -X POST \ -H 'Content-Type: application/json' \ -d '{"input": {"image": "https://.../input.jpg"}}' ```
Or, combine build and run via the serve command:
```console $ cog serve -p 8080
$ curl http://localhost:8080/predictions -X POST \ -H 'Content-Type: application/json' \ -d '{"input": {"image": "https://.../input.jpg"}}' ```
Why are we building this?
It's really hard for researchers to ship machine learning models to production.
Part of the solution is Docker, but it is so complex to get it to work: Dockerfiles, pre-/post-processing, Flask servers, CUDA versions. More often than not the researcher has to sit down with an engineer to get the damn thing deployed.
Andreas and Ben created Cog. Andreas used to work at Spotify, where he built tools for building and deploying ML models with Docker. Ben worked at Docker, where he created Docker Compose.
We realized that, in addition to Spotify, other companies were also using Docker to build and deploy machine learning models. Uber and others have built similar systems. So, we're making an open source version so other people can do this too.
Hit us up if you're interested in using it or want to collaborate with us. We're on Discord or email us at team@replicate.com.
Prerequisites
- macOS, Linux or Windows 11. Cog works on macOS, Linux and Windows 11 with WSL 2
- Docker. Cog uses Docker to create a container for your model. You'll need to install Docker before you can run Cog. If you install Docker Engine instead of Docker Desktop, you will need to install Buildx as well.
Install
If you're using macOS, you can install Cog using Homebrew:
console
brew install cog
You can also download and install the latest release using our install script:
```sh
fish shell
sh (curl -fsSL https://cog.run/install.sh | psub)
bash, zsh, and other shells
sh <(curl -fsSL https://cog.run/install.sh)
download with wget and run in a separate command
wget -qO- https://cog.run/install.sh sh ./install.sh ```
You can manually install the latest release of Cog directly from GitHub by running the following commands in a terminal:
console
sudo curl -o /usr/local/bin/cog -L "https://github.com/replicate/cog/releases/latest/download/cog_$(uname -s)_$(uname -m)"
sudo chmod +x /usr/local/bin/cog
Alternatively, you can build Cog from source and install it with these commands:
console
make
sudo make install
Or if you are on docker:
RUN sh -c "INSTALL_DIR=\"/usr/local/bin\" SUDO=\"\" $(curl -fsSL https://cog.run/install.sh)"
Upgrade
If you're using macOS and you previously installed Cog with Homebrew, run the following:
console
brew upgrade cog
Otherwise, you can upgrade to the latest version by running the same commands you used to install it.
Next steps
- Get started with an example model
- Get started with your own model
- Using Cog with notebooks
- Using Cog with Windows 11
- Take a look at some examples of using Cog
- Deploy models with Cog
cog.yamlreference to learn how to define your model's environment- Prediction interface reference to learn how the
Predictorinterface works - Training interface reference to learn how to add a fine-tuning API to your model
- HTTP API reference to learn how to use the HTTP API that models serve
Need help?
Contributors ✨
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
Owner
- Name: Replicate
- Login: replicate
- Kind: organization
- Email: team@replicate.com
- Website: https://replicate.com
- Twitter: replicate
- Repositories: 158
- Profile: https://github.com/replicate
Run AI with an API
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Ben Firshman | b****n@f****k | 313 |
| dependabot[bot] | 4****] | 220 |
| andreasjansson | a****s@r****i | 211 |
| Nick Stenning | n****k@w****m | 156 |
| Will Sackfield | s****d@r****m | 133 |
| Mattt | m****t@r****m | 131 |
| Zeke Sikelianos | z****e@s****m | 98 |
| Dominic Baggott | d****t@g****m | 98 |
| allcontributors[bot] | 4****] | 62 |
| technillogue | t****e@g****m | 37 |
| Philip Potter | p****l@r****m | 27 |
| F | f@r****m | 22 |
| Dan Buch | d****n@m****m | 19 |
| Neville Li | n****h@g****m | 19 |
| Michael Dwan | m@d****o | 17 |
| Dmitri Khokhlov | d****v@g****m | 17 |
| Aron Carroll | a****n@r****m | 16 |
| synek | r****y@r****o | 13 |
| Nikhil Sinha | 5****1 | 12 |
| Morgan Fainberg | c****e@t****t | 11 |
| Yorick | y****k@y****l | 8 |
| Jesse Andrews | j****e@r****m | 8 |
| Hongchao Deng | h****1@g****m | 7 |
| Marcus Martins | m****s@r****m | 7 |
| Simon Eskildsen | s****p@s****m | 6 |
| Aron Carroll | c****e@a****m | 5 |
| EduarteXD | i@m****n | 3 |
| omahs | 7****s | 3 |
| Michael Floering | m****g@g****m | 3 |
| André Knörig | a****g | 2 |
| and 40 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 470
- Total pull requests: 1,606
- Average time to close issues: 4 months
- Average time to close pull requests: 30 days
- Total issue authors: 275
- Total pull request authors: 95
- Average comments per issue: 1.96
- Average comments per pull request: 0.75
- Merged pull requests: 976
- Bot issues: 3
- Bot pull requests: 440
Past Year
- Issues: 91
- Pull requests: 731
- Average time to close issues: 7 days
- Average time to close pull requests: 7 days
- Issue authors: 72
- Pull request authors: 37
- Average comments per issue: 1.38
- Average comments per pull request: 0.41
- Merged pull requests: 440
- Bot issues: 2
- Bot pull requests: 178
Top Authors
Issue Authors
- bfirsh (40)
- mattt (21)
- zeke (17)
- 8W9aG (16)
- andreasjansson (10)
- nickstenning (10)
- tzktz (9)
- technillogue (6)
- sinopec (6)
- Clement-Lelievre (5)
- geoxpert0001 (5)
- yorickvP (4)
- sepehr (4)
- boxabirds (3)
- yairyairyair (3)
Pull Request Authors
- dependabot[bot] (423)
- 8W9aG (298)
- mattt (122)
- technillogue (120)
- aron (58)
- nickstenning (51)
- zeke (49)
- meatballhat (42)
- nevillelyh (42)
- michaeldwan (40)
- philandstuff (32)
- dkhokhlov (30)
- bfirsh (28)
- NikhilSinha1 (23)
- tempusfrangit (22)
Top Labels
Issue Labels
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Packages
- Total packages: 2
-
Total downloads:
- pypi 3,718,984 last-month
- Total docker downloads: 107,621
-
Total dependent packages: 3
(may contain duplicates) -
Total dependent repositories: 10
(may contain duplicates) - Total versions: 373
- Total maintainers: 2
pypi.org: cog
Containers for machine learning
- Documentation: https://cog.readthedocs.io/
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-
Latest release: 0.16.6
published 6 months ago
Rankings
Maintainers (2)
proxy.golang.org: github.com/replicate/cog
- Homepage: https://github.com/replicate/cog
- Documentation: https://pkg.go.dev/github.com/replicate/cog#section-documentation
- License: Apache-2.0
-
Latest release: v0.16.6
published 6 months ago
Rankings
Dependencies
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- github.com/matttproud/golang_protobuf_extensions v1.0.1
- github.com/mbilski/exhaustivestruct v1.2.0
- github.com/mgechev/revive v1.3.2
- github.com/mholt/archiver/v3=>github.com/bfirsh/archiver/v3 v3.5.1-0.20210316180101-755470a1a69b
- github.com/mitchellh/go-homedir v1.1.0
- github.com/mitchellh/mapstructure v1.5.0
- github.com/moby/term v0.5.0
- github.com/mohae/deepcopy v0.0.0-20170929034955-c48cc78d4826
- github.com/moricho/tparallel v0.3.1
- github.com/nakabonne/nestif v0.3.1
- github.com/nishanths/exhaustive v0.11.0
- github.com/nishanths/predeclared v0.2.2
- github.com/nunnatsa/ginkgolinter v0.13.5
- github.com/olekukonko/tablewriter v0.0.5
- github.com/opencontainers/go-digest v1.0.0
- github.com/opencontainers/image-spec v1.0.2
- github.com/pelletier/go-toml v1.9.5
- github.com/pelletier/go-toml/v2 v2.0.5
- github.com/perimeterx/marshmallow v1.1.5
- github.com/pkg/errors v0.9.1
- github.com/pmezard/go-difflib v1.0.0
- github.com/polyfloyd/go-errorlint v1.4.4
- github.com/prometheus/client_golang v1.12.1
- github.com/prometheus/client_model v0.2.0
- github.com/prometheus/common v0.32.1
- github.com/prometheus/procfs v0.7.3
- github.com/quasilyte/go-ruleguard v0.4.0
- github.com/quasilyte/gogrep v0.5.0
- github.com/quasilyte/regex/syntax v0.0.0-20210819130434-b3f0c404a727
- github.com/quasilyte/stdinfo v0.0.0-20220114132959-f7386bf02567
- github.com/rivo/uniseg v0.2.0
- github.com/ryancurrah/gomodguard v1.3.0
- github.com/ryanrolds/sqlclosecheck v0.4.0
- github.com/sanposhiho/wastedassign/v2 v2.0.7
- github.com/sashamelentyev/interfacebloat v1.1.0
- github.com/sashamelentyev/usestdlibvars v1.24.0
- github.com/securego/gosec/v2 v2.17.0
- github.com/shazow/go-diff v0.0.0-20160112020656-b6b7b6733b8c
- github.com/sirupsen/logrus v1.9.3
- github.com/sivchari/containedctx v1.0.3
- github.com/sivchari/nosnakecase v1.7.0
- github.com/sivchari/tenv v1.7.1
- github.com/sonatard/noctx v0.0.2
- github.com/sourcegraph/go-diff v0.7.0
- github.com/spf13/afero v1.8.2
- github.com/spf13/cast v1.5.0
- github.com/spf13/cobra v1.7.0
- github.com/spf13/jwalterweatherman v1.1.0
- github.com/spf13/pflag v1.0.5
- github.com/spf13/viper v1.13.0
- github.com/ssgreg/nlreturn/v2 v2.2.1
- github.com/stbenjam/no-sprintf-host-port v0.1.1
- github.com/stretchr/objx v0.5.0
- github.com/stretchr/testify v1.8.4
- github.com/subosito/gotenv v1.4.1
- github.com/t-yuki/gocover-cobertura v0.0.0-20180217150009-aaee18c8195c
- github.com/tdakkota/asciicheck v0.2.0
- github.com/tetafro/godot v1.4.14
- github.com/timakin/bodyclose v0.0.0-20230421092635-574207250966
- github.com/timonwong/loggercheck v0.9.4
- github.com/tomarrell/wrapcheck/v2 v2.8.1
- github.com/tommy-muehle/go-mnd/v2 v2.5.1
- github.com/ultraware/funlen v0.1.0
- github.com/ultraware/whitespace v0.0.5
- github.com/uudashr/gocognit v1.0.7
- github.com/vincent-petithory/dataurl v1.0.0
- github.com/xeipuuv/gojsonpointer v0.0.0-20180127040702-4e3ac2762d5f
- github.com/xeipuuv/gojsonreference v0.0.0-20180127040603-bd5ef7bd5415
- github.com/xeipuuv/gojsonschema v1.2.0
- github.com/xen0n/gosmopolitan v1.2.1
- github.com/xeonx/timeago v1.0.0-rc5
- github.com/yagipy/maintidx v1.0.0
- github.com/yeya24/promlinter v0.2.0
- github.com/ykadowak/zerologlint v0.1.3
- gitlab.com/bosi/decorder v0.4.0
- go.tmz.dev/musttag v0.7.2
- go.uber.org/atomic v1.7.0
- go.uber.org/multierr v1.6.0
- go.uber.org/zap v1.24.0
- golang.org/x/exp v0.0.0-20230510235704-dd950f8aeaea
- golang.org/x/exp/typeparams v0.0.0-20230307190834-24139beb5833
- golang.org/x/mod v0.12.0
- golang.org/x/net v0.15.0
- golang.org/x/sync v0.3.0
- golang.org/x/sys v0.12.0
- golang.org/x/term v0.12.0
- golang.org/x/text v0.13.0
- golang.org/x/tools v0.13.0
- google.golang.org/protobuf v1.28.0
- gopkg.in/fsnotify.v1=>github.com/kolaente/fsnotify v1.4.10-0.20200411160148-1bc3c8ff4048
- gopkg.in/ini.v1 v1.67.0
- gopkg.in/yaml.v2 v2.4.0
- gopkg.in/yaml.v3 v3.0.1
- gotest.tools/gotestsum v1.11.0
- honnef.co/go/tools v0.4.5
- mvdan.cc/gofumpt v0.5.0
- mvdan.cc/interfacer v0.0.0-20180901003855-c20040233aed
- mvdan.cc/lint v0.0.0-20170908181259-adc824a0674b
- mvdan.cc/unparam v0.0.0-20221223090309-7455f1af531d
- sigs.k8s.io/yaml v1.3.0
- 855 dependencies
- PyYAML *
- attrs >=20.1,<24
- fastapi >=0.75.2,<0.99.0
- pydantic >=1.9,<2
- requests >=2,<3
- structlog >=20,<24
- typing-compat python_version < "3.8"
- typing_extensions >=4.1.0
- uvicorn [standard]>=0.12,<1