# How can we deploy ML model (.zip) to nodes locally, not via SSL or the firewall

**URL:** <https://forum.opensearch.org/t/how-can-we-deploy-ml-model-zip-to-nodes-locally-not-via-ssl-or-the-firewall/20438>\
**Category:** OpenSearch\
**Tags:** discuss, troubleshoot, configure, install\
**Created:** [July 22, 2024, 10:34am UTC](https://forum.opensearch.org/t/how-can-we-deploy-ml-model-zip-to-nodes-locally-not-via-ssl-or-the-firewall/20438 "2024-07-22T10:34:34Z")\
**Posts on this page:** 1\
**Showing post:** 9

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**Author:** ![yeonghyeonKo](https://sea1.discourse-cdn.com/flex019/user_avatar/forum.opensearch.org/yeonghyeonko/32/8202_2.png) [@yeonghyeonKo](https://forum.opensearch.org/u/yeonghyeonKo)\
**Post date:** [August 19, 2024, 1:32am UTC](https://forum.opensearch.org/t/how-can-we-deploy-ml-model-zip-to-nodes-locally-not-via-ssl-or-the-firewall/20438/9 "2024-08-19T01:32:50Z")

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@zane_neo Before I’ve tried inject configuration files for ML Model in `spec.nodePools[].env`, it’s necessary to check how many and what kind of files should be included. So I tested two environments each,

1. one is using a **public internet** environment tested at home and
2. the other is still a restricted environment because of **closed network** in the company.  
(There should be proxy issue when using Java Process to download files)

First, when I tried to register/deploy ML model in a public internet env, I can’t see any error or exception about SSL so that the model is successfully deployed to all nodes in the OpenSearch Cluster. Inside the pod(running for a ML node), there are newly added folders( **pytorch** and **tokenizers** ) in `opensearch/data/ml_cache` including the below files.

```bash
$ tree

├── pytorch
│ ├── 1.13.1-cpu-precxx11-linux-x86_64
│ │ ├── 0.28.0-libdjl_torch.so
│ │ ├── libc10.so
│ │ ├── libgomp-a34b3233.so.1
│ │ ├── libstdc++.so.6
│ │ ├── libtorch.so
│ │ └── libtorch_cpu.so
│ └── 1.13.1.txt
└── tokenizers
    └── 0.19.1-0.28.0-linux-x86_64
        └── libtokenizers.so

```

So I extracted two folders with all files from container to localhost, and then sent them the Closed-Network env. Finally I used the below codes to put them in containers from localhost.

```auto
$ docker cp pytorch/ opensearch-node1:/usr/share/opensearch/data/ml_cache
$ docker cp pytorch/ opensearch-node2:/usr/share/opensearch/data/ml_cache
$ docker cp tokenizers/ opensearch-node1:/usr/share/opensearch/data/ml_cache
$ docker cp tokenizers/ opensearch-node2:/usr/share/opensearch/data/ml_cache

```

  

With this method, we can easily pre-include torch files with a new docker image like:

```Dockerfile
FROM opensearchproject/opensearch:2.16.0

# Switch to root user for installation
USER root

# Set the working directory
WORKDIR /usr/share/opensearch

# Copy the necessary files
COPY ./pytorch /data/ml_cache
COPY ./tokenizers /data/ml_cache

# Grant execute permissions to the script
RUN chmod +x /data/ml_cache/pytorch
RUN chmod +x /data/ml_cache/tokenizers

# Switch back to the default non-root user
USER opensearch

```

In this scenario, without using `spec.nodePools[].env`, I could deployed ML models in the Closed-Network environment.

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