feat(conda): conda environments (#1144)

* feat(autogptq): add a separate conda environment for autogptq (#1137)

**Description**

This PR related to #1117

**Notes for Reviewers**

Here we lock down the version of the dependencies. Make sure it can be
used all the time without failed if the version of dependencies were
upgraded.

I change the order of importing packages according to the pylint, and no
change the logic of code. It should be ok.

I will do more investigate on writing some test cases for every backend.
I can run the service in my environment, but there is not exist a way to
test it. So, I am not confident on it.

Add a README.md in the `grpc` root. This is the common commands for
creating `conda` environment. And it can be used to the reference file
for creating extral gRPC backend document.

Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* [Extra backend] Add seperate environment for ttsbark (#1141)

**Description**

This PR relates to #1117

**Notes for Reviewers**

Same to the latest PR:
* The code is also changed, but only the order of the import package
parts. And some code comments are also added.
* Add a configuration of the `conda` environment
* Add a simple test case for testing if the service can be startup in
current `conda` environment. It is succeed in VSCode, but the it is not
out of box on terminal. So, it is hard to say the test case really
useful.

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* feat(conda): add make target and entrypoints for the dockerfile

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(conda): Add seperate conda env for diffusers (#1145)

**Description**

This PR relates to  #1117

**Notes for Reviewers**

* Add `conda` env `diffusers.yml`
* Add Makefile to create it automatically
* Add `run.sh` to support running as a extra backend
  * Also adding it to the main Dockerfile
* Add make command in the root Makefile
* Testing the server, it can start up under the env

Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(conda):Add seperate env for vllm (#1148)

**Description**

This PR is related to #1117

**Notes for Reviewers**

* The gRPC server can be started as normal
* The test case can be triggered in VSCode
* Same to other this kind of PRs, add `vllm.yml` Makefile and add
`run.sh` to the main Dockerfile, and command to the main Makefile

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* feat(conda):Add seperate env for huggingface (#1146)

**Description**

This PR is related to  #1117

**Notes for Reviewers**

* Add conda env `huggingface.yml`
* Change the import order, and also remove the no-used packages
* Add `run.sh` and `make command` to the main Dockerfile and Makefile
* Add test cases for it. It can be triggered and succeed under VSCode
Python extension but it is hang by using `python -m unites
test_huggingface.py` in the terminal

```
Running tests (unittest): /workspaces/LocalAI/extra/grpc/huggingface
Running tests: /workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_embedding
/workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_load_model
/workspaces/LocalAI/extra/grpc/huggingface/test_huggingface.py::TestBackendServicer::test_server_startup
./test_huggingface.py::TestBackendServicer::test_embedding Passed

./test_huggingface.py::TestBackendServicer::test_load_model Passed

./test_huggingface.py::TestBackendServicer::test_server_startup Passed

Total number of tests expected to run: 3
Total number of tests run: 3
Total number of tests passed: 3
Total number of tests failed: 0
Total number of tests failed with errors: 0
Total number of tests skipped: 0

Finished running tests!
```

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* feat(conda): Add the seperate conda env for VALL-E X (#1147)

**Description**

This PR is related  to #1117

**Notes for Reviewers**

* The gRPC server cannot start up

```
(ttsvalle) @Aisuko ➜ /workspaces/LocalAI (feat/vall-e-x) $ /opt/conda/envs/ttsvalle/bin/python /workspaces/LocalAI/extra/grpc/vall-e-x/ttsvalle.py
Traceback (most recent call last):
  File "/workspaces/LocalAI/extra/grpc/vall-e-x/ttsvalle.py", line 14, in <module>
    from utils.generation import SAMPLE_RATE, generate_audio, preload_models
ModuleNotFoundError: No module named 'utils'
```

The installation steps follow
https://github.com/Plachtaa/VALL-E-X#-installation below:

* Under the `ttsvalle` conda env

```
git clone https://github.com/Plachtaa/VALL-E-X.git
cd VALL-E-X
pip install -r requirements.txt
```

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* fix: set image type

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(conda):Add seperate conda env for exllama (#1149)

Add seperate env for exllama

Signed-off-by: Aisuko <urakiny@gmail.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Setup conda

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Set image_type arg

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* ci: prepare only conda env in tests

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Dockerfile: comment manual pip calls

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* conda: add conda to PATH

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fixes

* add shebang

* Fixups

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* file perms

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* debug

* Install new conda in the worker

* Disable GPU tests for now until the worker is back

* Rename workflows

* debug

* Fixup conda install

* fixup(wrapper): pass args

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: GitHub <noreply@github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Aisuko <urakiny@gmail.com>
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
Co-authored-by: Aisuko <urakiny@gmail.com>

extra/grpc/vllm/backend_vllm.py

@@ -1,15 +1,15 @@
 #!/usr/bin/env python3
-import grpc
 from concurrent import futures
 import time
-import backend_pb2
-import backend_pb2_grpc
 import argparse
 import signal
 import sys
-import os, glob
+import os
 
-from pathlib import Path
+import backend_pb2
+import backend_pb2_grpc
+
+import grpc
 from vllm import LLM, SamplingParams
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
@@ -19,7 +19,20 @@ MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
 
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
+    """
+    A gRPC servicer that implements the Backend service defined in backend.proto.
+    """
     def generate(self,prompt, max_new_tokens):
+        """
+        Generates text based on the given prompt and maximum number of new tokens.
+
+        Args:
+            prompt (str): The prompt to generate text from.
+            max_new_tokens (int): The maximum number of new tokens to generate.
+
+        Returns:
+            str: The generated text.
+        """
         self.generator.end_beam_search()
 
         # Tokenizing the input
@@ -41,9 +54,31 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             if token.item() == self.generator.tokenizer.eos_token_id:
                 break
         return decoded_text
+
     def Health(self, request, context):
+        """
+        Returns a health check message.
+
+        Args:
+            request: The health check request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Reply: The health check reply.
+        """
         return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+
     def LoadModel(self, request, context):
+        """
+        Loads a language model.
+
+        Args:
+            request: The load model request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The load model result.
+        """
         try:
             if request.Quantization != "":
                 self.llm = LLM(model=request.Model, quantization=request.Quantization)
@@ -54,6 +89,16 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         return backend_pb2.Result(message="Model loaded successfully", success=True)
 
     def Predict(self, request, context):
+        """
+        Generates text based on the given prompt and sampling parameters.
+
+        Args:
+            request: The predict request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The predict result.
+        """
         if request.TopP == 0:
             request.TopP = 0.9
 
@@ -68,6 +113,16 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         return backend_pb2.Result(message=bytes(generated_text, encoding='utf-8'))
 
     def PredictStream(self, request, context):
+        """
+        Generates text based on the given prompt and sampling parameters, and streams the results.
+
+        Args:
+            request: The predict stream request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The predict stream result.
+        """
         # Implement PredictStream RPC
         #for reply in some_data_generator():
         #    yield reply
@@ -104,4 +159,4 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    serve(args.addr)
+    serve(args.addr)