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VictorSanh
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5c41c59
1
Parent(s):
217780a
Update visualization
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README.md
CHANGED
@@ -5,7 +5,7 @@ colorFrom: red
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.12.0
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app_file:
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pinned: false
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---
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@@ -21,6 +21,6 @@ The process of adding a model to the main space:
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- Use `huggingface-cli login` to login with an auth token that has a read/write access to the `HuggingFaceM4` org on the hub.
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- Use `./upload_checkpoint_to_hub_gcs.sh` script to upload a checkpoint from GCP store to the hub. An example command to upload checkpoint for step 3000 from `tr_121ter` to the hub: `./m4/visualization/upload_checkpoint_to_hub_gcs.sh gs://hf-science-m4-cold/local_experiment_dir/tr_121ter/opt_step-3000`. This will create model repo under the `HuggingFaceM4` repo on the hub. If you are on the cluster, use `./upload_checkpoint_to_hub_s3.sh` instead. I recommend being on a compute node to avoid disk space issues (uploading to the hub consists in downloading locally the checkpoint, creating a repo on the hub, copying it locally, filling it with the weights and commiting the weights to the hub repo).
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- [MANUAL] Go to the hub, create a repo of type `space` with the same name as the model. In the space's settings, add a secret `HF_AUTH_TOKEN` with a token which has read access to the `HuggingFaceM4` repo. This step can be potentially automated in the future.
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- [MANUAL] Edit `m4/visualization/
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- Run `m4/visualization/sync-repo.sh <name_of_the_space_on_the_hub>` to sync the repo with the local setting. This will automatically update the space to have the latest code as in the `m4/visualization/
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- Run `m4/visualization/sync-repo.sh main` to update the main repo as well with the new model.
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.12.0
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app_file: app_dialogue.py
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pinned: false
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---
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- Use `huggingface-cli login` to login with an auth token that has a read/write access to the `HuggingFaceM4` org on the hub.
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- Use `./upload_checkpoint_to_hub_gcs.sh` script to upload a checkpoint from GCP store to the hub. An example command to upload checkpoint for step 3000 from `tr_121ter` to the hub: `./m4/visualization/upload_checkpoint_to_hub_gcs.sh gs://hf-science-m4-cold/local_experiment_dir/tr_121ter/opt_step-3000`. This will create model repo under the `HuggingFaceM4` repo on the hub. If you are on the cluster, use `./upload_checkpoint_to_hub_s3.sh` instead. I recommend being on a compute node to avoid disk space issues (uploading to the hub consists in downloading locally the checkpoint, creating a repo on the hub, copying it locally, filling it with the weights and commiting the weights to the hub repo).
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- [MANUAL] Go to the hub, create a repo of type `space` with the same name as the model. In the space's settings, add a secret `HF_AUTH_TOKEN` with a token which has read access to the `HuggingFaceM4` repo. This step can be potentially automated in the future.
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- [MANUAL] Edit `m4/visualization/app_dialogue.py`'s three dictionary to include your model in the existing formats of those dictionaries.
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- Run `m4/visualization/sync-repo.sh <name_of_the_space_on_the_hub>` to sync the repo with the local setting. This will automatically update the space to have the latest code as in the `m4/visualization/app_dialogue.py`.
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- Run `m4/visualization/sync-repo.sh main` to update the main repo as well with the new model.
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