From 75e623bd9b15d438e206768702af232e8ff148e5 Mon Sep 17 00:00:00 2001
From: henrique-lh <henrique.santos.lhls@gmail.com>
Date: Wed, 9 Jul 2025 11:14:10 -0300
Subject: [PATCH 1/5] - fix: send env file

---
 src/mlops_codex/preprocessing.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/mlops_codex/preprocessing.py b/src/mlops_codex/preprocessing.py
index d34cf48..68274b5 100644
--- a/src/mlops_codex/preprocessing.py
+++ b/src/mlops_codex/preprocessing.py
@@ -182,7 +182,7 @@ def __upload_script(
         token: str,
     ) -> None:
         """
-        Upload python script to MLOps.
+        Upload a python script to MLOps.
 
         Parameters
         ----------
@@ -430,6 +430,7 @@ def create(
         schema_files_path: Optional[
             Union[Tuple[str, str], List[Tuple[str, str]]]
         ] = None,
+        env_file: Optional[str] = None,
         schema_datasets: Optional[Union[str, List[str]]] = None,
         extra_files: Union[Tuple[str, str], List[Tuple[str, str]]] = None,
         wait_read: bool = False,
@@ -556,6 +557,17 @@ def create(
         )
         logger.info("Requirements file uploaded")
 
+        if env_file:
+            make_request(
+                url=f"{self.base_url}/v2/preprocessing/{preprocessing_script_hash}/env-file",
+                method='PATCH',
+                success_code=201,
+                files={'env': open(env_file, 'rb')},
+                headers={
+                    "Authorization": f"Bearer {token}",
+                },
+            )
+
         logger.info("Hosting preprocessing script")
 
         if extra_files is not None:
@@ -2417,6 +2429,7 @@ def create(
                 requirements_path=requirements_file,
                 python_version=python_version,
                 schema_files_path=schema,
+                env_file=env,
                 extra_files=extra_files,
                 wait_read=wait_complete,
             )

From b99519da649cc28aab6180504fd7e6dd5d1990a2 Mon Sep 17 00:00:00 2001
From: Luis Henrique <75594301+henrique-lh@users.noreply.github.com>
Date: Fri, 4 Jul 2025 15:02:38 -0300
Subject: [PATCH 2/5] Remove training v2 (#90)

* - remove training v2

* - remove training v2

* remove training v2

* remove files with errors

* - fix broken python versions (lightgbm)
- remove fixed versions from pandas, scikit-learn
- create a new pipfile.lock

* - force version of urllib3

* - remove 3.8 python

* - rollback
---
 .github/workflows/test.yml                    |    2 +-
 Pipfile                                       |   20 +-
 Pipfile.lock                                  |  875 ++++++-------
 src/mlops_codex/trainingv2/Training.ipynb     |  560 ---------
 src/mlops_codex/trainingv2/__init__.py        |    7 -
 src/mlops_codex/trainingv2/base.py            |  256 ----
 src/mlops_codex/trainingv2/commons.py         |   44 -
 src/mlops_codex/trainingv2/training.py        | 1097 -----------------
 .../trainingv2/training_executions.py         |  783 ------------
 src/mlops_codex/trainingv2/trigger.py         |  303 -----
 src/mlops_codex/trainingv2/validations.py     |   16 -
 11 files changed, 451 insertions(+), 3512 deletions(-)
 delete mode 100644 src/mlops_codex/trainingv2/Training.ipynb
 delete mode 100644 src/mlops_codex/trainingv2/__init__.py
 delete mode 100644 src/mlops_codex/trainingv2/base.py
 delete mode 100644 src/mlops_codex/trainingv2/commons.py
 delete mode 100644 src/mlops_codex/trainingv2/training.py
 delete mode 100644 src/mlops_codex/trainingv2/training_executions.py
 delete mode 100644 src/mlops_codex/trainingv2/trigger.py
 delete mode 100644 src/mlops_codex/trainingv2/validations.py

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 4dc642d..2ae4ac4 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-22.04
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
diff --git a/Pipfile b/Pipfile
index 7f1a0dc..cddab70 100644
--- a/Pipfile
+++ b/Pipfile
@@ -7,16 +7,16 @@ name = "pypi"
 mlops_codex = {path = "."}
 
 [dev-packages]
-notebook = "==6.5.2"
+notebook = "=6.5.2"
 ipykernel = "*"
-pydata-sphinx-theme = "==0.12.0"
-sphinx = "==6.1.3"
-sphinx-intl = "==2.1.0"
+pydata-sphinx-theme = "=0.12.0"
+sphinx = "=6.1.3"
+sphinx-intl = "=2.1.0"
 m2r2 = "*"
 twine = "*"
-pandas = "==1.4.0"
-lightgbm = "==3.3.2"
-scikit-learn = "==1.5.1"
-pyaml="==24.9.0"
-pydantic="==2.10.4"
-ruff="==0.9.4"
\ No newline at end of file
+pandas = ">=1.4.0"
+lightgbm = "=4.6.0"
+scikit-learn = ">=1.5.1"
+pyaml="=24.9.0"
+pydantic="=2.10.4"
+ruff="=0.9.4"
\ No newline at end of file
diff --git a/Pipfile.lock b/Pipfile.lock
index 6db11ef..a3a7697 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "05de04106aff3665cb0f9586f9e1dc0e17ed08980a575d42a755fb04ddef7702"
+            "sha256": "725901af0714cec5a04eddea3b868d2648813eaac410487c43de385a01779d7c"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -24,19 +24,19 @@
         },
         "cachetools": {
             "hashes": [
-                "sha256:82e73ba88f7b30228b5507dce1a1f878498fc669d972aef2dde4f3a3c24f103e",
-                "sha256:f225782b84438f828328fc2ad74346522f27e5b1440f4e9fd18b20ebfd1aa2cf"
+                "sha256:1c7bb3cf9193deaf3508b7c5f2a79986c13ea38965c5adcff1f84519cf39163e",
+                "sha256:b4c4f404392848db3ce7aac34950d17be4d864da4b8b66911008e430bc544587"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==6.0.0"
+            "version": "==6.1.0"
         },
         "certifi": {
             "hashes": [
-                "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6",
-                "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"
+                "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057",
+                "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==2025.4.26"
+            "markers": "python_version >= '3.7'",
+            "version": "==2025.6.15"
         },
         "charset-normalizer": {
             "hashes": [
@@ -145,7 +145,7 @@
             "version": "==3.1.1"
         },
         "datarisk-mlops-codex": {
-            "version": "==2.2.8rc0"
+            "version": "==2.2.10"
         },
         "idna": {
             "hashes": [
@@ -473,11 +473,11 @@
         },
         "python-dotenv": {
             "hashes": [
-                "sha256:1684eb44636dd462b66c3ee016599815514527ad99965de77f43e0944634a7e5",
-                "sha256:b77d08274639e3d34145dfa6c7008e66df0f04b7be7a75fd0d5292c191d79045"
+                "sha256:31f23644fe2602f88ff55e1f5c79ba497e01224ee7737937930c448e4d0e24dc",
+                "sha256:a8a6399716257f45be6a007360200409fce5cda2661e3dec71d23dc15f6189ab"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.21.0"
+            "markers": "python_version >= '3.9'",
+            "version": "==1.1.1"
         },
         "pytz": {
             "hashes": [
@@ -547,11 +547,11 @@
         },
         "requests": {
             "hashes": [
-                "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760",
-                "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"
+                "sha256:27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c",
+                "sha256:27d0316682c8a29834d3264820024b62a36942083d52caf2f14c0591336d3422"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.32.3"
+            "version": "==2.32.4"
         },
         "ruff": {
             "hashes": [
@@ -587,11 +587,11 @@
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4",
-                "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af"
+                "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36",
+                "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==4.14.0"
+            "version": "==4.14.1"
         },
         "tzdata": {
             "hashes": [
@@ -603,11 +603,11 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466",
-                "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"
+                "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760",
+                "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==2.4.0"
+            "version": "==2.5.0"
         }
     },
     "develop": {
@@ -637,11 +637,11 @@
         },
         "argon2-cffi": {
             "hashes": [
-                "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08",
-                "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"
+                "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1",
+                "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==23.1.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==25.1.0"
         },
         "argon2-cffi-bindings": {
             "hashes": [
@@ -712,11 +712,11 @@
         },
         "beautifulsoup4": {
             "hashes": [
-                "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b",
-                "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"
+                "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b",
+                "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"
             ],
             "markers": "python_full_version >= '3.7.0'",
-            "version": "==4.13.3"
+            "version": "==4.13.4"
         },
         "bleach": {
             "hashes": [
@@ -728,11 +728,11 @@
         },
         "certifi": {
             "hashes": [
-                "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6",
-                "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"
+                "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057",
+                "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==2025.4.26"
+            "markers": "python_version >= '3.7'",
+            "version": "==2025.6.15"
         },
         "cffi": {
             "hashes": [
@@ -907,11 +907,11 @@
         },
         "click": {
             "hashes": [
-                "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2",
-                "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"
+                "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202",
+                "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==8.1.8"
+            "markers": "python_version >= '3.10'",
+            "version": "==8.2.1"
         },
         "comm": {
             "hashes": [
@@ -923,46 +923,46 @@
         },
         "cryptography": {
             "hashes": [
-                "sha256:0339a692de47084969500ee455e42c58e449461e0ec845a34a6a9b9bf7df7fb8",
-                "sha256:03dbff8411206713185b8cebe31bc5c0eb544799a50c09035733716b386e61a4",
-                "sha256:06509dc70dd71fa56eaa138336244e2fbaf2ac164fc9b5e66828fccfd2b680d6",
-                "sha256:0cf13c77d710131d33e63626bd55ae7c0efb701ebdc2b3a7952b9b23a0412862",
-                "sha256:23b9c3ea30c3ed4db59e7b9619272e94891f8a3a5591d0b656a7582631ccf750",
-                "sha256:25eb4d4d3e54595dc8adebc6bbd5623588991d86591a78c2548ffb64797341e2",
-                "sha256:2882338b2a6e0bd337052e8b9007ced85c637da19ef9ecaf437744495c8c2999",
-                "sha256:3530382a43a0e524bc931f187fc69ef4c42828cf7d7f592f7f249f602b5a4ab0",
-                "sha256:425a9a6ac2823ee6e46a76a21a4e8342d8fa5c01e08b823c1f19a8b74f096069",
-                "sha256:46cf7088bf91bdc9b26f9c55636492c1cce3e7aaf8041bbf0243f5e5325cfb2d",
-                "sha256:4828190fb6c4bcb6ebc6331f01fe66ae838bb3bd58e753b59d4b22eb444b996c",
-                "sha256:49fe9155ab32721b9122975e168a6760d8ce4cffe423bcd7ca269ba41b5dfac1",
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-                "sha256:627ba1bc94f6adf0b0a2e35d87020285ead22d9f648c7e75bb64f367375f3b22",
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-                "sha256:f3fe7a5ae34d5a414957cc7f457e2b92076e72938423ac64d215722f6cf49a9e"
+                "sha256:0027d566d65a38497bc37e0dd7c2f8ceda73597d2ac9ba93810204f56f52ebc7",
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+                "sha256:1e1da5accc0c750056c556a93c3e9cb828970206c68867712ca5805e46dc806f",
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+                "sha256:2089cc8f70a6e454601525e5bf2779e665d7865af002a5dec8d14e561002e135",
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+                "sha256:3fcfbefc4a7f332dece7272a88e410f611e79458fab97b5efe14e54fe476f4fd",
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+                "sha256:57c816dfbd1659a367831baca4b775b2a5b43c003daf52e9d57e1d30bc2e1b0e",
+                "sha256:5aa1e32983d4443e310f726ee4b071ab7569f58eedfdd65e9675484a4eb67bd1",
+                "sha256:6ff8728d8d890b3dda5765276d1bc6fb099252915a2cd3aff960c4c195745dd0",
+                "sha256:7259038202a47fdecee7e62e0fd0b0738b6daa335354396c6ddebdbe1206af2a",
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+                "sha256:8c4a6ff8a30e9e3d38ac0539e9a9e02540ab3f827a3394f8852432f6b0ea152e",
+                "sha256:9024beb59aca9d31d36fcdc1604dd9bbeed0a55bface9f1908df19178e2f116e",
+                "sha256:90cb0a7bb35959f37e23303b7eed0a32280510030daba3f7fdfbb65defde6a97",
+                "sha256:91098f02ca81579c85f66df8a588c78f331ca19089763d733e34ad359f474174",
+                "sha256:926c3ea71a6043921050eaa639137e13dbe7b4ab25800932a8498364fc1abec9",
+                "sha256:982518cd64c54fcada9d7e5cf28eabd3ee76bd03ab18e08a48cad7e8b6f31b18",
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@@ -1141,11 +1141,11 @@
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@@ -2423,11 +2435,11 @@
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             "hashes": [
-                "sha256:18fd474d4a82a5f83dac888df697af65afa82dec7323d09c3e37d1f14288da54",
-                "sha256:3e386e96793c8702ae83d17b853fb93d3e09ef82ec62722e61da5cd22376dcd8"
+                "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922",
+                "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==78.1.0"
+            "version": "==80.9.0"
         },
         "six": {
             "hashes": [
@@ -2447,18 +2459,19 @@
         },
         "snowballstemmer": {
             "hashes": [
-                "sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1",
-                "sha256:c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a"
+                "sha256:6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064",
+                "sha256:6d5eeeec8e9f84d4d56b847692bacf79bc2c8e90c7f80ca4444ff8b6f2e52895"
             ],
-            "version": "==2.2.0"
+            "markers": "python_version not in '3.0, 3.1, 3.2'",
+            "version": "==3.0.1"
         },
         "soupsieve": {
             "hashes": [
-                "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb",
-                "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9"
+                "sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4",
+                "sha256:ad282f9b6926286d2ead4750552c8a6142bc4c783fd66b0293547c8fe6ae126a"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.6"
+            "version": "==2.7"
         },
         "sphinx": {
             "hashes": [
@@ -2594,19 +2607,19 @@
         },
         "types-python-dateutil": {
             "hashes": [
-                "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb",
-                "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"
+                "sha256:13e80d6c9c47df23ad773d54b2826bd52dbbb41be87c3f339381c1700ad21ee5",
+                "sha256:2b2b3f57f9c6a61fba26a9c0ffb9ea5681c9b83e69cd897c6b5f668d9c0cab93"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==2.9.0.20241206"
+            "markers": "python_version >= '3.9'",
+            "version": "==2.9.0.20250516"
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4",
-                "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af"
+                "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36",
+                "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==4.14.0"
+            "version": "==4.14.1"
         },
         "tzdata": {
             "hashes": [
@@ -2626,11 +2639,11 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466",
-                "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"
+                "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760",
+                "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==2.4.0"
+            "version": "==2.5.0"
         },
         "wcwidth": {
             "hashes": [
@@ -2662,14 +2675,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==1.8.0"
         },
-        "wheel": {
-            "hashes": [
-                "sha256:661e1abd9198507b1409a20c02106d9670b2576e916d58f520316666abca6729",
-                "sha256:708e7481cc80179af0e556bbf0cc00b8444c7321e2700b8d8580231d13017248"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.45.1"
-        },
         "zipp": {
             "hashes": [
                 "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e",
diff --git a/src/mlops_codex/trainingv2/Training.ipynb b/src/mlops_codex/trainingv2/Training.ipynb
deleted file mode 100644
index 80b3637..0000000
--- a/src/mlops_codex/trainingv2/Training.ipynb
+++ /dev/null
@@ -1,560 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "77559178",
-   "metadata": {},
-   "source": [
-    "# MLOps Training\n",
-    "\n",
-    "This notebook give a exemple on how to use MLOps to training a ML model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18c9b0da",
-   "metadata": {},
-   "source": [
-    "### MLOpsTrainingClient\n",
-    "\n",
-    "It's where you can manage your trainining experiments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "initial_id",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-30T12:15:41.835327Z",
-     "start_time": "2025-04-30T12:15:41.479994Z"
-    },
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from mlops_codex.training import MLOpsTrainingClient"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f47d2a5a",
-   "metadata": {
-    "vscode": {
-     "languageId": "markdown"
-    }
-   },
-   "source": [
-    "### Initializing the MLOpsTrainingClient\n",
-    "In this cell, we are initializing the `MLOpsTrainingClient` which will be used to manage our training experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "556a3fb73290a75b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-30T12:15:46.482827Z",
-     "start_time": "2025-04-30T12:15:44.210969Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "May 7, 2025 | INFO: __init__ Loading .env\n",
-      "May 7, 2025 | INFO: __init__ Successfully connected to MLOps\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "API version 1.0 \n",
-       " Token=\"eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6IlFnc0JWQ0I5WFc0V1YtSkVCVkJiZyJ9.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.clMUNe_MRV6VA66Zsrmv3RSQUVsU3dvVTFY1i9bFWtMEZTOOieKRPakUEgRO-f9bu3yWY3Lm5vyUywCxIY6nL5fFHKOdfqKA38hzuTfL9n_oiNnuGhQZJc6OmtC7eylvCM4Bqde0KqTU4aAXk2jOA3ny1EoEbYfkprtVwv26njC5rJqLhc_kQMtJ4cHvziWzZo_ft3vWIimmTniJqqOz5K2KX2hURUwpbEXUSSoe056a8OspGBYCrzR8zr592y9ReTeSfoL86HP-AMQeAXH4CKoD_MMJKcoOX93TW03agFx1S87vtJBJIk6k7lkQfIZYGPmdX1yA4rsSBcvqN1pmRA"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "client = MLOpsTrainingClient()\n",
-    "client"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "deb3a4c9",
-   "metadata": {},
-   "source": [
-    "## MLOpsTrainingExperiment\n",
-    "\n",
-    "It's where you can create a training experiment to find the best model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09074b27",
-   "metadata": {},
-   "source": [
-    "#### Custom training\n",
-    "\n",
-    "With Custom training, you have to create the training function. For you, as a data scientist, it's common to re-run the entire notebook, over and over. To avoid creating the same experiment repeatedly, the `force = False` parameter will disallow it. If you wish to create a new experiment with the same attributes, turn `force = True`.\n",
-    "\n",
-    "If you have two equal experiments and pass `force = False`, the first created experiment will be chosen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a8f129be78149bb1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-30T12:15:48.753762Z",
-     "start_time": "2025-04-30T12:15:48.637597Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "May 7, 2025 | INFO: create_training_experiment Trying to load experiment...\n",
-      "May 7, 2025 | INFO: __get_repeated_thash Found experiment with same attributes...\n",
-      "May 7, 2025 | INFO: __init__ Loading .env\n",
-      "May 7, 2025 | INFO: __init__ Successfully connected to MLOps\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating a new training experiment\n",
-    "training = client.create_training_experiment(\n",
-    "    experiment_name='experiment',\n",
-    "    model_type='Classification',\n",
-    "    group='datarisk',\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "be81cf47890e1e6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MLOpsTrainingExperiment(name=\"experiment\", \n",
-       "                                                        group=\"datarisk\", \n",
-       "                                                        training_id=\"T6cc61022f2640698545be2b931489921f29c9bae8844bc694361ee1a1d14918\",\n",
-       "                                                        model_type=Classification\n",
-       "                                                        )"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7a4107da1c07f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "May 7, 2025 | INFO: __upload_training Result\n",
-      "DatasetHash: D4b0221dd4ea48039e78aaecfe9516fcb77852751c1045bfb28d5c622ffa5733\n",
-      "ExecutionId: 3\n",
-      "Message: Training files have been uploaded! Use the id '3' to execute the train experiment.\n",
-      "\n",
-      "May 7, 2025 | INFO: __execute_training Model training starting - Hash: T6cc61022f2640698545be2b931489921f29c9bae8844bc694361ee1a1d14918\n",
-      "May 7, 2025 | INFO: __init__ Loading .env\n",
-      "May 7, 2025 | INFO: __init__ Successfully connected to MLOps\n",
-      "May 7, 2025 | INFO: __init__ Loading .env\n",
-      "Waiting the training run.."
-     ]
-    }
-   ],
-   "source": [
-    "# With the experiment class we can create multiple model runs\n",
-    "PATH = './samples/train/'\n",
-    "\n",
-    "run = training.run_training(\n",
-    "    run_name='First test',\n",
-    "    training_type='Custom',\n",
-    "    train_data=PATH + 'dados.csv',\n",
-    "    requirements_file=PATH + 'requirements.txt',\n",
-    "    source_file=PATH + 'app.py',\n",
-    "    python_version='3.9',\n",
-    "    training_reference='train_model',\n",
-    "    wait_complete=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a83032179dcb070",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c9162e4384c578a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.model_type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c90b801e64c17ee",
-   "metadata": {},
-   "source": [
-    "##### Copying a training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a279e58c101ddc94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "copy_run = run.copy_execution(\n",
-    "    train_data=PATH + 'dados.csv',\n",
-    "    requirements_file=PATH + 'requirements.txt',\n",
-    "    source_file=PATH + 'app.py',\n",
-    "    python_version='3.9',\n",
-    "    training_reference='train_model',\n",
-    "    wait_complete=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad830a9081ae8646",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "copy_run.execution_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "527d95c3f53eeed3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "copy_run.status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b55fb7e4ad997e8",
-   "metadata": {},
-   "source": [
-    "### Promote training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "61bd54e3b648fdfd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PATH = './samples/asyncModel/'\n",
-    "model = run.promote(\n",
-    "    source_file_path=PATH + 'app.py',\n",
-    "    schema_path=PATH + 'schema.csv',\n",
-    "    operation=\"Async\",\n",
-    "    model_name=\"AsyncModel\",\n",
-    "    input_type=\".csv\",\n",
-    "    model_reference=\"score\",\n",
-    "    wait_complete=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "daedbd49",
-   "metadata": {},
-   "source": [
-    "#### AutoML\n",
-    "\n",
-    "With AutoML you just need to upload the data and some configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "816f785a26dcf617",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PATH = './samples/autoML/'\n",
-    "\n",
-    "run2 = training.run_training(\n",
-    "    run_name='First test',\n",
-    "    training_type='AutoML',\n",
-    "    conf_dict=PATH + \"conf.json\",\n",
-    "    train_data=PATH + 'dados.csv',\n",
-    "    wait_complete=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44281a357e0028c6",
-   "metadata": {},
-   "source": [
-    "#### External Training\n",
-    "\n",
-    "Besides the autoML and custom training, you can perform a training on your own machine and upload the files!\n",
-    "\n",
-    "Look the example bellow\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad876be96a8213ab",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-30T12:37:48.767708Z",
-     "start_time": "2025-04-30T12:36:45.349008Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "April 30, 2025 | INFO: validate Validating external training execution...\n",
-      "April 30, 2025 | INFO: __init__ Loading .env\n",
-      "April 30, 2025 | INFO: __init__ Successfully connected to MLOps\n",
-      "April 30, 2025 | INFO: register_execution Training Execution '12' created for First test\n",
-      "April 30, 2025 | INFO: send_file Features for execution was created from file!\n",
-      "April 30, 2025 | INFO: send_file Dataset hash = D80b29eba7fd4a89887bc45d98d660d1de8f530ade5b47629b6437d96f574d5d\n",
-      "April 30, 2025 | INFO: send_file Target for execution was created from file!\n",
-      "April 30, 2025 | INFO: send_file Dataset hash = D4f3ff1032d84e0d89bd29e82232e9a361110aacb20f4b21a3be01c8f34894d5\n",
-      "April 30, 2025 | INFO: send_file Output for execution was created from file!\n",
-      "April 30, 2025 | INFO: send_file Dataset hash = D95f28a782a8449ba63d5526521de0f2ea281b6ba2b341eab213bf53bab96ff8\n",
-      "April 30, 2025 | INFO: send_file Metrics for execution 12 was created from file!\n",
-      "April 30, 2025 | INFO: send_file Parameters for execution 12 was created from file!\n",
-      "April 30, 2025 | INFO: send_file Model for execution 12 was created from file!\n",
-      "April 30, 2025 | INFO: send_file Requirements file inserted for execution '12'\n",
-      "April 30, 2025 | INFO: send_json {\"Python version patched for training with hash 'Tdae4e5bdd874c5d9e94c9a2aee73d22a13e925cadef49668b82df6a68180ccc' and id '12'\"}\n",
-      "April 30, 2025 | INFO: host Training execution started. Use its execution id 12 to check the status.\n",
-      "Training your model.....\n",
-      "April 30, 2025 | INFO: wait_ready Training finished successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "PATH = './samples/externalUpload/'\n",
-    "\n",
-    "run3 = training.run_training(\n",
-    "    run_name='First test',\n",
-    "    training_type=\"External\",\n",
-    "    features_file=PATH + 'features.parquet',\n",
-    "    target_file=PATH + 'target.parquet',\n",
-    "    output_file=PATH + 'predictions.parquet',\n",
-    "    metrics_file=PATH + 'metrics.json',\n",
-    "    parameters_file=PATH + 'params.json',\n",
-    "    requirements_file=PATH + 'requirements.txt',\n",
-    "    model_file=PATH + 'model.pkl',\n",
-    "    python_version=\"3.9\",\n",
-    "    wait_complete=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21dbdbe6a258990c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-30T12:37:52.625919Z",
-     "start_time": "2025-04-30T12:37:52.567819Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Succeeded'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run3.status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f55fe0a26c5c2221",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "#### Interactive External Training\n",
-    "\n",
-    "However, if you wish something more interactive, take a look in the example bellow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d8b38fedf20383b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlops_codex.training import MLOpsTrainingClient\n",
-    "client = MLOpsTrainingClient()\n",
-    "training = client.create_training_experiment(\n",
-    "    experiment_name='Teste',\n",
-    "    model_type='Classification',\n",
-    "    group='<group>'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "764cec758141737c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from lightgbm import LGBMClassifier\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.model_selection import cross_val_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b22680b507344617",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_path = './samples/train/'\n",
-    "df = pd.read_csv(base_path+\"/dados.csv\")\n",
-    "X = df.drop(columns=['target'])\n",
-    "y = df[[\"target\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3200db0515a2b3c3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.scatter(df[\"mean_radius\"], df[\"mean_texture\"])\n",
-    "\n",
-    "# Configurar o título do gráfico\n",
-    "plt.title(\"Relação entre mean_radius e mean_texture\")\n",
-    "\n",
-    "# Configurar os rótulos dos eixos\n",
-    "plt.xlabel(\"mean_radius\")\n",
-    "plt.ylabel(\"mean_texture\")\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "\n",
-    "# Exibir o gráfico\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d748960244448dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pipe = make_pipeline(SimpleImputer(), LGBMClassifier(force_col_wise=True))\n",
-    "pipe.fit(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7dbcffb925124081",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with training.log_train(name='Teste 2', X_train=X, y_train=y) as logger:\n",
-    "    logger.save_model(pipe)\n",
-    "\n",
-    "    model_output = pd.DataFrame({\"pred\": pipe.predict(X), \"proba\": pipe.predict_proba(X)[:,1]})\n",
-    "\n",
-    "    logger.save_model_output(model_output)\n",
-    "\n",
-    "    logger.save_plot(fig=fig, filename=\"test-image\")\n",
-    "\n",
-    "    auc = cross_val_score(pipe, X, y, cv=5, scoring=\"roc_auc\")\n",
-    "    f_score = cross_val_score(pipe, X, y, cv=5, scoring=\"f1\")\n",
-    "    logger.save_metric(name='auc', value=auc.mean())\n",
-    "    logger.save_metric(name='f1_score', value=f_score.mean())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c57288178cbe64a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/mlops_codex/trainingv2/__init__.py b/src/mlops_codex/trainingv2/__init__.py
deleted file mode 100644
index 51b1142..0000000
--- a/src/mlops_codex/trainingv2/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .training import MLOpsTrainingExperiment, MLOpsTrainingClient, MLOpsTrainingLogger
-
-__all__ = [
-    "MLOpsTrainingExperiment",
-    "MLOpsTrainingClient",
-    "MLOpsTrainingLogger",
-]
\ No newline at end of file
diff --git a/src/mlops_codex/trainingv2/base.py b/src/mlops_codex/trainingv2/base.py
deleted file mode 100644
index 22cca76..0000000
--- a/src/mlops_codex/trainingv2/base.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import abc
-from time import sleep
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-from mlops_codex.__model_states import ModelExecutionState
-from mlops_codex.base import BaseMLOps
-from mlops_codex.exceptions import TrainingError, InputError
-from mlops_codex.http_request_handler import make_request, refresh_token
-from mlops_codex.logger_config import get_logger
-from mlops_codex.model import SyncModel, AsyncModel
-
-logger = get_logger()
-
-
-class ITrainingExecution(BaseModel, abc.ABC):
-    """
-    Interface for training execution.
-
-    Parameters
-    ----------
-    training_hash: str
-        Training hash.
-    group: str
-        Group where the training is inserted.
-    model_type: str
-        Type of the model. It must be 'Custom', 'AutoML' or 'External'
-    execution_id: int
-        Execution ID of a training.
-    experiment_name: str
-        Name of the experiment.
-    login: str
-        Login credential.
-    password: str
-        Password credential.
-    url: str
-        Url used to connect to the MLOps server.
-    mlops_class: BaseMLOps
-        MLOps class instance.
-    """
-
-    training_hash: str = Field(
-        frozen=True, title="Training hash", validate_default=True
-    )
-    group: str = Field(frozen=True, title="Group", validate_default=True)
-    model_type: str = Field(frozen=True, title="Model type", validate_default=True)
-
-    execution_id: int = Field(default=None, gt=0)
-    experiment_name: str = Field(default=None)
-
-    login: str = Field(default=None, repr=False)
-    password: str = Field(default=None, repr=False)
-    url: str = Field(default="https://neomaril.datarisk.net/", repr=False)
-    mlops_class: BaseMLOps = Field(default=None, repr=False)
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    def model_post_init(self, __context: Any) -> None:
-        """
-        Initializes the model after creation.
-
-        Parameters
-        ----------
-        __context: Any
-            Context for initialization.
-        """
-        if self.mlops_class is None:
-            self.mlops_class = BaseMLOps(
-                login=self.login, password=self.password, url=self.url
-            )
-
-        url = f"{self.mlops_class.base_url}/training/describe/{self.group}/{self.training_hash}"
-        token = refresh_token(*self.mlops_class.credentials, self.mlops_class.base_url)
-
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            custom_exception=TrainingError,
-            custom_exception_message=f'Experiment "{self.training_hash}" not found.',
-            specific_error_code=404,
-            logger_msg=f'Experiment "{self.training_hash}" not found.',
-            headers={
-                "Authorization": f"Bearer {token}",
-            },
-        )
-
-        training_data = response.json()["Description"]
-        self.experiment_name = training_data["ExperimentName"]
-
-    @property
-    def status(self) -> str:
-        """
-        Gets the current status of the execution.
-
-        Returns
-        -------
-        str
-            Current status of the execution.
-
-        Raises
-        ------
-        TrainingError
-            If the execution is not found.
-        AuthenticationError
-            If the authentication fails.
-        """
-        url = f"{self.mlops_class.base_url}/v2/training/execution/{self.execution_id}/status"
-        token = refresh_token(*self.mlops_class.credentials, self.mlops_class.base_url)
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            custom_exception=TrainingError,
-            custom_exception_message=f"Experiment with execution id {self.execution_id} not found.",
-            specific_error_code=404,
-            logger_msg=f"Experiment with execution id {self.execution_id} not found.",
-            headers={
-                "Authorization": f"Bearer {token}",
-            },
-        ).json()
-
-        status = response["Status"]
-        if status == "Failed":
-            msg = response["Message"]
-            logger.info(msg)
-
-        return status
-
-    def host(self):
-        """
-        Hosts the current execution.
-        """
-        url = f"{self.mlops_class.base_url}/v2/training/execution/{self.execution_id}"
-        token = refresh_token(*self.mlops_class.credentials, self.mlops_class.base_url)
-
-        response = make_request(
-            url=url,
-            method="PATCH",
-            success_code=202,
-            headers={
-                "Authorization": f"Bearer {token}",
-                "Neomaril-Origin": "Codex",
-                "Neomaril-Method": self.host.__qualname__,
-            },
-        ).json()
-
-        msg = response["Message"]
-        logger.info(msg)
-
-    def wait_ready(self):
-        """
-        Waits until the model is ready.
-        """
-        current_status = ModelExecutionState.Running
-        print("Training your model...", end="", flush=True)
-        while current_status in [
-            ModelExecutionState.Running,
-            ModelExecutionState.Requested,
-        ]:
-            current_status = ModelExecutionState[self.status]
-            sleep(30)
-            print(".", end="", flush=True)
-        print()
-
-        if current_status == ModelExecutionState.Succeeded:
-            logger.info("Training finished successfully.")
-        else:
-            logger.info(f"Training failed. Current status is {current_status}")
-
-    @abc.abstractmethod
-    def _promote(self, *args, **kwargs):
-        pass
-
-    def promote(self, *args, **kwargs):
-        """
-        Abstract method to promote the execution.
-
-        Parameters
-        ----------
-        args: tuple
-            Positional arguments.
-        kwargs
-            Keyword arguments.
-        """
-
-        operation: str = kwargs["operation"]
-
-        if operation not in ["Sync", "Async"]:
-            raise InputError("Operation must be either 'Sync' or 'Async'.")
-
-        wait_complete = kwargs.pop("wait_complete", False)
-        model_hash = self._promote(*args, **kwargs)
-        builder = SyncModel if operation else AsyncModel
-        model = builder(
-            name=kwargs["model_name"],
-            model_hash=model_hash,
-            login=self.login,
-            password=self.password,
-            url=self.url,
-            group=self.group
-        )
-        model.host(operation)
-        if wait_complete:
-            model.wait_ready()
-        return model
-
-    def execution_info(self):
-        """
-        Abstract method to get execution information.
-        """
-        raise NotImplementedError("Execution info is not implemented.")
-
-    def copy_execution(self, **kwargs):
-        url = f"{self.mlops_class.base_url}/v2/training/execution/{self.execution_id}/copy"
-        token = refresh_token(*self.mlops_class.credentials, self.mlops_class.base_url)
-        return self._do_copy(
-            url, token, self.group, self.experiment_name, self.mlops_class, **kwargs
-        )
-
-    @classmethod
-    @abc.abstractmethod
-    def _do_copy(cls, url, token, group, experiment_name, mlops_class, **kwargs):
-        """
-        Abstract method to copy the execution.
-
-        Parameters
-        ----------
-        url: str
-            URL to copy the execution.
-        token: str
-            Authentication token.
-        group: str
-            Group where the training is inserted.
-        experiment_name: str
-            Name of the experiment.
-        mlops_class: BaseMLOps
-            MLOps class instance.
-        kwargs: dict
-            Extra arguments passed to the specific function.
-        """
-        pass
-
-    @abc.abstractmethod
-    def _update_execution(self, **kwargs):
-        """
-        Abstract method to update the execution
-
-        Parameters
-        ----------
-        kwargs: dict
-            Extra arguments passed to the specific function.
-        """
-        pass
diff --git a/src/mlops_codex/trainingv2/commons.py b/src/mlops_codex/trainingv2/commons.py
deleted file mode 100644
index 9d0e2de..0000000
--- a/src/mlops_codex/trainingv2/commons.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from mlops_codex.http_request_handler import make_request
-from mlops_codex.logger_config import get_logger
-
-logger = get_logger()
-
-
-def register_execution(url, token, run_name, description, training_type):
-    """
-    Register a training execution.
-
-    Parameters
-    ----------
-    url: str
-        URL to register the execution.
-    token: str
-        Authentication token.
-    run_name: str
-        Name of the run.
-    description: str
-        Description of the training.
-    training_type: str
-        Type of training.
-    """
-    register_training_response = make_request(
-        url=url,
-        method="POST",
-        success_code=201,
-        json={
-            "RunName": run_name,
-            "Description": description,
-            "TrainingType": training_type,
-        },
-        headers={
-            "Authorization": f"Bearer {token}",
-            "Neomaril-Origin": "Codex",
-            "Neomaril-Method": "Register execution",
-        },
-    ).json()
-
-    msg = register_training_response.get("Message")
-    execution_id = register_training_response["ExecutionId"]
-    logger.info(f"{msg} for {run_name}")
-
-    return execution_id
diff --git a/src/mlops_codex/trainingv2/training.py b/src/mlops_codex/trainingv2/training.py
deleted file mode 100644
index c5bb1e5..0000000
--- a/src/mlops_codex/trainingv2/training.py
+++ /dev/null
@@ -1,1097 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-import json
-import os
-import re
-import sys
-from contextlib import contextmanager
-from typing import Any, Optional, Union
-
-import cloudpickle
-import numpy as np
-import pandas as pd
-from lazy_imports import try_import
-
-from mlops_codex.__utils import parse_json_to_yaml
-from mlops_codex.base import BaseMLOps, BaseMLOpsClient
-from mlops_codex.dataset import validate_dataset
-from mlops_codex.datasources import MLOpsDataset
-from mlops_codex.exceptions import (
-    InputError,
-    TrainingError,
-)
-from mlops_codex.http_request_handler import make_request, refresh_token
-from mlops_codex.logger_config import get_logger
-from mlops_codex.shared import constants
-from mlops_codex.shared.utils import parse_data
-from mlops_codex.trainingv2.base import ITrainingExecution
-from mlops_codex.trainingv2.training_executions import (
-    AutoMLTrainingExecution,
-    CustomTrainingExecution,
-    ExternalTrainingExecution,
-)
-from mlops_codex.validations import validate_group_existence
-
-patt = re.compile(r"(\d+)")
-logger = get_logger()
-
-
-class MLOpsTrainingLogger:
-    """A class for logging MLOps training runs.
-
-    Example
-    -------
-
-    .. code-block:: python
-        with training.log_train('Teste 1', X, y) as logger:
-            pipe.fit(X, y)
-            logger.save_model(pipe)
-
-            params = pipe.get_params()
-            params.pop('steps')
-            params.pop('simpleimputer')
-            params.pop('lgbmclassifier')
-            logger.save_params(params)
-
-            model_output = pd.DataFrame({"pred": pipe.predict(X), "proba": pipe.predict_proba(X)[:,1]})
-            logger.save_model_output(model_output)
-
-            auc = cross_val_score(pipe, X, y, cv=5, scoring="roc_auc")
-            f_score = cross_val_score(pipe, X, y, cv=5, scoring="f1")
-            logger.save_metric(name='auc', value=auc.mean())
-            logger.save_metric(name='f1_score', value=f_score.mean())
-
-            logger.set_python_version('3.10')
-    """
-
-    def __init__(
-        self,
-        *,
-        name: str,
-        X_train: pd.DataFrame,
-        y_train: pd.DataFrame,
-        save_path: Optional[str] = None,
-    ):
-        self.name = name
-        self.X_train = X_train
-        self.y_train = y_train
-        self.output = None
-        self.model = None
-        self.metrics = {}
-        self.params = {}
-        self.python_version = f"{sys.version_info.major}.{sys.version_info.minor}"
-        self.extras = []
-
-        # Paths
-        self.features_file = None
-        self.target_file = None
-        self.output_file = None
-        self.metrics_file = None
-        self.params_file = None
-        self.requirements = None
-        self.model_file = None
-
-        if not save_path:
-            dir_name = self.name.replace(" ", "_")
-            save_path = f"./{dir_name}"
-
-        os.makedirs(save_path, exist_ok=True)
-        self.save_path = save_path
-
-    def _processing_logging_inputs(self):
-        """
-        Processing of everything that be logged and return object
-        """
-
-        self.__set_params()
-        self.params_file = self.__to_json("params", self.params)
-
-        self.features_file = self.__to_parquet(
-            output_filename="features",
-            input_data=self.__parse_data_objects(self.X_train),
-        )
-
-        self.target_file = self.__to_parquet(
-            output_filename="target", input_data=self.__parse_data_objects(self.y_train)
-        )
-
-        self.output_file = self.__to_parquet(
-            output_filename="predictions",
-            input_data=self.__parse_data_objects(self.output),
-        )
-
-        if self.model:
-            self.model_file = self.__to_pickle(
-                output_filename="model", input_data=self.model
-            )
-
-        if self.metrics:
-            self.metrics_file = self.__to_json("metrics", self.metrics)
-
-    def save_model(self, model):
-        """
-        Save the trained model to the logger.
-
-        Parameters
-        ----------
-        model: object
-            The trained model.
-        """
-        self.model = model
-
-    def save_metric(self, *, name, value):
-        """
-        Save a metric to the logger.
-
-        Parameters
-        ----------
-        name: str
-            The name of the metric.
-        value: float
-            The value of the metric.
-        """
-        self.metrics[name] = value
-
-    def save_model_output(self, output):
-        """
-        Save the model output to the logger.
-
-        Parameters
-        ----------
-        output: object
-            The output of the trained model.
-        """
-        self.output = output
-
-    def set_python_version(self, version: str):
-        """
-        Set the Python version used to train the model.
-
-        Parameters
-        ----------
-        version: str
-            The Python version.
-        """
-        self.python_version = version
-
-    def set_requirements(self, requirements: str):
-        """
-        Set the project requirements.
-
-        Parameters
-        ----------
-        requirements: str
-            The path of project requirements.
-        """
-        self.requirements = requirements
-
-    def save_plot(
-        self, *, fig: object, filename: str, dpi: int = 300, ext: str = "png"
-    ):
-        """
-        Save plot graphic image to the logger.
-
-        Parameters
-        ----------
-        fig: matplotlib.figure.Figure, seaborn.axisgrid.FacetGrid, seaborn.axes._subplots.AxesSubplot plotly.graph_objects.Figure.
-            Figure object
-        filename: str
-            filename without extension (extension will be added automatically).
-        dpi: int, default=300
-            Resolution for matplotlib/seaborn plots. Default is 300.
-        ext: str, default='png'
-            File format to save (e.g., 'png', 'pdf', 'svg', 'html'). If None, defaults to 'png' for static images.
-
-        Raises
-        ------
-        TypeError
-            If the figure type is not supported.
-        """
-
-        filepath = f"{self.save_path}/{filename}.{ext}"
-
-        with try_import() as _:
-            import plotly
-
-            if isinstance(fig, plotly.graph_objs.Figure):
-                self.__save_plotly_plot(fig=fig, filepath=filepath)
-                return
-
-        with try_import() as _:
-            import matplotlib.pyplot as plt
-            import seaborn as sns
-
-            if isinstance(fig, sns.axisgrid.FacetGrid) or isinstance(fig, plt.Figure):
-                self.__save_seaborn_or_matplotlib_plot(
-                    fig=fig, dpi=dpi, filepath=filepath
-                )
-                return
-
-        raise TypeError("The plot only accepts plots of Matplotlib/Plotly/Seaborn")
-
-    def __save_plotly_plot(self, fig, filepath):
-        """
-        Save a plotly figure to the logger.
-
-        Parameters
-        ----------
-        fig: plotly.graph_objects.Figure
-            The figure to save.
-        filepath: str
-            Path to the file to save.
-        **kwargs:
-            Extra keyword arguments passed to savefig() or write_image()/write_html().
-        """
-
-        if filepath.endswith("html"):
-            fig.write_html(f"{filepath}")
-            return
-
-        fig.write_image(f"{filepath}")
-        self.add_extra(extra=filepath)
-
-    def __save_seaborn_or_matplotlib_plot(self, *, fig, dpi, filepath):
-        fig.savefig(filepath, dpi=dpi)
-        self.add_extra(extra=filepath)
-
-    def set_extra(self, extra: list):
-        """
-        Set the extra files list.
-
-        Parameters
-        ----------
-        extra: list
-            A list of paths to the extra files.
-        """
-        self.extras = extra
-
-    def add_extra(self, *, extra: Union[pd.DataFrame, str], filename: str = None):
-        """
-        Add an extra file in the extra file list.
-
-        Parameters
-        ----------
-        extra: Union[pd.DataFrame, str]
-            A path of an extra file or a list to include in extra file list.
-        filename: Optional[str], optional
-            A filename if the extra is a DataFrame.
-        """
-
-        if isinstance(extra, str):
-            if os.path.exists(extra):
-                self.extras.append(extra)
-            else:
-                raise FileNotFoundError("Extra file path not found!")
-        elif isinstance(extra, pd.DataFrame):
-            if filename:
-                self.extras.append(
-                    self.__to_parquet(output_filename=filename, input_data=extra)
-                )
-            else:
-                raise InputError("Needs a filename to save the dataframe parquet.")
-        else:
-            raise InputError("Extra must be a Pandas DataFrame or a path.")
-
-    def add_requirements(self, filename: str):
-        """
-        Add a requirement file.
-
-        Parameters
-        ----------
-        filename: str
-            The name of the output filename to save.
-        """
-        self.requirements = filename
-
-    def __to_parquet(self, *, output_filename: str, input_data: pd.DataFrame):
-        """
-        Transform dataframe to parquet.
-
-        Args:
-            output_filename: The name of output filename to save.
-            input_data: A pandas dataframe to save.
-        """
-        path = os.path.join(self.save_path, f"{output_filename}.parquet")
-        input_data.to_parquet(path)
-        self.add_extra(extra=path)
-        return path
-
-    def __to_json(self, output_filename: str, input_data: dict):
-        """
-        Transform dict to JSON.
-
-        Args:
-            output_filename: The name of the output filename to save.
-            input_data: A dictionary to save.
-        """
-        path = os.path.join(self.save_path, f"{output_filename}.json")
-        with open(path, mode="w", encoding="utf-8") as json_file:
-            json.dump(input_data, json_file)
-        self.add_extra(extra=path)
-        return path
-
-    def __to_pickle(self, *, output_filename: str, input_data):
-        """
-        Transform content to pickle.
-
-        Args:
-            output_filename: The name of the output filename to save.
-            input_data: The content to save.
-        """
-        path = os.path.join(self.save_path, f"{output_filename}.pkl")
-        with open(path, "wb") as f:
-            cloudpickle.dump(input_data, f)
-        self.add_extra(extra=path)
-        return path
-
-    def __set_params(self):
-        """
-        Set parameters for training.
-        """
-        missing = self.X_train.isna().sum()
-        missing_dict = {
-            k + "_missings": v
-            for k, v in missing[missing > 0].describe().to_dict().items()
-            if k != "count"
-        }
-
-        params = {
-            "shape": self.X_train.shape,
-            "cols_with_missing": len(missing[missing > 0]),
-            "missing_distribution": missing_dict,
-        }
-
-        try:
-            params["pipeline_steps"] = list(self.model.named_steps.keys())
-        except Exception:
-            params["pipeline_steps"] = [
-                str(self.model.__class__).replace("<class '", "").replace("'>", "")
-            ]
-
-        if "get_all_params" in dir(self.model):
-            hyperparameters = {
-                f"hyperparam_{k}": str(v)
-                for k, v in self.model.get_all_params().items()
-                if k != "task_type"
-            }
-        elif "get_params" in dir(self.model):
-            hyperparameters = {
-                "hyperparam_" + k: str(v)
-                for k, v in self.model.get_params().items()
-                if k not in params["pipeline_steps"] + ["steps", "memory", "verbose"]
-            }
-
-            params = {**params, **hyperparameters}
-
-        if len(self.y_train.value_counts()) < 10:
-            target_proportion = self.y_train.value_counts() / len(self.y_train)
-            target_proportion = target_proportion.to_dict()
-            target_proportion = [
-                {"target": k, "proportion": v} for k, v in target_proportion.items()
-            ]
-            params["target_proportion"] = target_proportion
-        else:
-            params["target_distribution"] = {
-                k: v
-                for k, v in self.y_train.describe().to_dict().items()
-                if k != "count"
-            }
-
-        self.params = {**params, **self.params}
-
-    @staticmethod
-    def __parse_data_objects(obj: Any) -> pd.DataFrame:
-        """
-        Transform data types to dataframe
-        """
-        if isinstance(obj, pd.Series):
-            return obj.to_frame()
-        elif isinstance(obj, (np.ndarray, list, tuple, dict)):
-            array_df = pd.DataFrame(obj)
-            array_df.columns = [str(c) for c in array_df.columns]
-            return array_df
-        elif isinstance(obj, pd.DataFrame):
-            return obj
-        else:
-            raise TypeError(f"{obj} couldn't be a DataFrame")
-
-
-class MLOpsTrainingExperiment(BaseMLOps):
-    """
-    Class to manage models being trained inside MLOps
-
-    Parameters
-    ----------
-    login: str
-        Login for authenticating with the client. You can also use the env variable MLOPS_USER to set this
-    password: str
-        Password for authenticating with the client. You can also use the env variable MLOPS_PASSWORD to set this
-    training_hash: str
-        Training id (hash) from the experiment you want to access
-    group: str
-        Group the training is inserted.
-    environment: str
-        Flag that choose which environment of MLOps you are using. Test your deployment first before changing to production. Default is True
-    executions: List[int]
-        Ids for the executions in that training
-
-
-    Raises
-    ------
-    TrainingError
-        When the training can't be accessed in the server
-    AuthenticationError
-        Invalid credentials
-
-    Example
-    -------
-
-    .. code-block:: python
-
-        from mlops_codex.training import MLOpsTrainingClient
-        from mlops_codex.base import MLOpsExecution
-
-        client = MLOpsTrainingClient('123456')
-        client.create_group('ex_group', 'Group for example purpose')
-        training = client.create_training_experiment('Training example', 'Classification', 'ex_group')
-        print(client.get_training(training.training_id, 'ex_group').training_data)
-
-        data_path = './samples/train/'
-
-        run = run = training.run_training('First test', data_path+'dados.csv', training_reference='train_model', training_type='Custom', python_version='3.9', requirements_file=data_path+'requirements.txt', wait_complete=True)
-
-        print(run.get_training_execution(run.exec_id))
-        print(run.download_result())
-    """
-
-    def __init__(
-        self,
-        *,
-        training_hash: str,
-        group: str,
-        login: Optional[str] = None,
-        password: Optional[str] = None,
-        url: Optional[str] = "https://neomaril.datarisk.net/",
-    ) -> None:
-        super().__init__(login=login, password=password, url=url)
-
-        self.training_hash = training_hash
-        self.group = group
-
-        url = f"{self.base_url}/training/describe/{self.group}/{self.training_hash}"
-        token = refresh_token(*self.credentials, self.base_url)
-
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            custom_exception=TrainingError,
-            custom_exception_message=f'Experiment "{training_hash}" not found.',
-            specific_error_code=404,
-            logger_msg=f'Experiment "{training_hash}" not found.',
-            headers={
-                "Authorization": f"Bearer {token}",
-            },
-        )
-
-        training_data = response.json()["Description"]
-        self.model_type = training_data["ModelType"]
-        self.experiment_name = training_data["ExperimentName"]
-
-    def __repr__(self) -> str:
-        return f"""MLOpsTrainingExperiment(name="{self.experiment_name}", 
-                                                        group="{self.group}", 
-                                                        training_id="{self.training_hash}",
-                                                        model_type={str(self.model_type)}
-                                                        )"""
-
-    def __str__(self):
-        return f'MLOPS training experiment "{self.experiment_name} (Group: {self.group}, Id: {self.training_hash})"'
-
-    def __describe(self):
-        """
-        Describe the training experiment.
-
-        Returns
-        -------
-        dict
-            Description of the training experiment.
-
-        Raises
-        ------
-        TrainingError
-            When the training can't be accessed in the server
-        AuthenticationError
-            Invalid credentials
-        """
-        url = f"{self.base_url}/v2/training/{self.training_hash}"
-        token = refresh_token(*self.credentials, self.base_url)
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            custom_exception=TrainingError,
-            headers={
-                "Authorization": f"Bearer {token}",
-                "Neomaril-Origin": "Codex",
-                "Neomaril-Method": self.__describe.__qualname__,
-            },
-        )
-
-        return response.json()
-
-    def project_info(self, mode="dict"):
-        """
-        Get the executions.
-
-        Parameters
-        ----------
-        mode: str, optional
-            The mode of the return value. Can be "dict" or "count". Default is "dict".
-
-        Returns
-        -------
-        Union[dict, int]
-            The executions in the specified mode.
-        """
-        describe = self.__describe()
-        if mode == "dict":
-            return describe
-        elif mode == "count":
-            return describe["ExperimentsQuantity"]
-        elif mode == "log":
-            yaml_data = parse_json_to_yaml(describe)
-            print(yaml_data)
-        else:
-            raise InputError(f"Invalid mode {mode}")
-
-    def run_training(
-        self,
-        *,
-        run_name: str,
-        training_type: str,
-        description: Optional[str] = None,
-        requirements_file: Optional[str] = None,
-        source_file: Optional[str] = None,
-        python_version: Optional[str] = "3.10",
-        training_reference: Optional[str] = None,
-        train_data: Optional[str] = None,
-        dataset: Union[str, MLOpsDataset] = None,
-        dataset_name: Optional[str] = "input",
-        conf_dict: Union[dict, str] = None,
-        features_file: Optional[str] = None,
-        features_hash: Optional[str] = None,
-        target_file: Optional[str] = None,
-        target_hash: Optional[str] = None,
-        output_file: Optional[str] = None,
-        output_hash: Optional[str] = None,
-        metrics_file: Optional[str] = None,
-        parameters_file: Optional[str] = None,
-        model_file: Optional[str] = None,
-        extra_files: Optional[list] = None,
-        env: Optional[str] = None,
-        wait_complete: Optional[bool] = False,
-    ) -> ITrainingExecution:
-        """
-        Runs a prediction from the current model.
-
-        Parameters
-        ---------
-        run_name: str
-            The execution name in case you'd like to search it later. Obrigatory for all training types
-        training_type: str
-            Either 'Custom', 'External' or 'AutoML'
-        description: Optional[str], default=None
-            A small description to help you remember the training
-        source_file: Optional[str], default=None
-            Path to the python source file. Obrigatory for 'Custom' training
-        python_version: Optional[str], default='3.10'
-            Version of the python executable to run. Obrigatory for 'Custom' training, optional for 'External' training
-            The available options are: '3.8, '3.9' and '3.10'
-        training_reference: Optional[str], default=None
-            Entrypoint function name in the source file. Obrigatory for 'Custom' training
-        requirements_file: Optional[str], default=None
-            The requirements.txt file. Obrigatory for 'Custom' training if you wish to install dependencies
-        train_data: Optional[str], default=None
-            Data used to train the model. Obrigatory for 'Custom' and 'AutoML' training types
-        dataset_name: Optional[str], default="input"
-            Provided name the input file. Obrigatory for 'Custom' and 'AutoML' training types
-        dataset: Union[str, MLOpsDataset], default=None
-            Dataset generated or uploaded from other MLOps modules. It is a string, it must be the dataset hash. You can
-            also provide the entire dataset class
-        conf_dict: Optional[dict, str], default=None
-            Configuration file or dict. It's obrigatory for 'AutoML' training. You'll provide necessary configuration
-            for dealing with your data and how to train the model
-        features_file: Optional[str], default=None
-            Path to the features file. Obrigatory for 'External' training
-        features_hash: Optional[str], default=None
-            Dataset hash that will be used as features. Obrigatory for 'External' training
-        target_file: Optional[str], default=None
-            Path to the target file. Obrigatory for 'External' training
-        target_hash: Optional[str], default=None
-            Dataset hash that will be used as target. Obrigatory for 'External' training
-        output_file: Optional[str], default=None
-            Path to the output file. Obrigatory for 'External' training
-        output_hash: Optional[str], default=None
-            Dataset hash that will be used as output. Obrigatory for 'External' training
-        metrics_file: Optional[str], default=None
-            Path to the metrics file. Obrigatory for 'External' training
-        parameters_file: Optional[str], default=None
-            Path to the parameter file. Obrigatory for 'External' training
-        model_file: Optional[str], default=None
-            Path to the model file. Obrigatory for 'External' training
-        extra_files: Optional[list], default=None
-            An optional list with a path of files used to train your model
-        env: Optional[str], default=None
-            An optional path to the provided environment variables
-        wait_complete: Optional[bool], default=False
-            Lock your script/cell until training is complete.
-        Raises
-        ------
-        AuthenticationError
-        InputError
-
-        Returns
-        -------
-        Example
-        -------
-        >>> execution = run = training.run_training(run_name=,training_type=,requirements_file=data_path+'requirements.txt',python_version='3.9',training_reference='train_model',wait_complete=True)
-        """
-
-        if training_type not in ("Custom", "External", "AutoML"):
-            raise InputError(
-                "Training type needs be: 'Custom', 'AutoML' or 'External'."
-            )
-
-        if dataset is None and train_data is None and training_type != "External":
-            raise InputError(
-                "You must provide a data to train your model. It can be a path to a file or a dataset hash."
-            )
-
-        if description is None:
-            description = f"Training is {training_type}"
-
-        if extra_files is None:
-            extra_files = []
-
-        if dataset is not None:
-            dataset_hash = validate_dataset(dataset)
-        else:
-            dataset_hash = None
-
-        input_data, upload_data = None, None
-
-        if training_type != "External":
-            input_data, upload_data = parse_data(
-                file_path=train_data,
-                form_data="dataset_hash" if dataset_hash is not None else "dataset_name",
-                file_name=dataset_name,
-                file_form="input",
-                dataset_hash=dataset_hash,
-            )
-
-        builder = {
-            "Custom": (
-                CustomTrainingExecution,
-                {
-                    "training_hash": self.training_hash,
-                    "model_type": training_type,
-                    "group": self.group,
-                    "login": self.credentials[0],
-                    "password": self.credentials[1],
-                    "url": self.base_url,
-                    "run_name": run_name,
-                    "description": description,
-                    "input_data": input_data,
-                    "upload_data": upload_data,
-                    "requirements_file": requirements_file,
-                    "source_file": source_file,
-                    "python_version": python_version,
-                    "training_reference": training_reference,
-                    "extra_files": extra_files,
-                    "env": env,
-                    "wait_complete": wait_complete,
-                },
-            ),
-            "AutoML": (
-                AutoMLTrainingExecution,
-                {
-                    "training_hash": self.training_hash,
-                    "model_type": training_type,
-                    "group": self.group,
-                    "login": self.credentials[0],
-                    "password": self.credentials[1],
-                    "url": self.base_url,
-                    "run_name": run_name,
-                    "description": description,
-                    "input_data": input_data,
-                    "upload_data": upload_data,
-                    "conf_dict": conf_dict,
-                    "extra_files": extra_files,
-                    "wait_complete": wait_complete,
-                },
-            ),
-            "External": (
-                ExternalTrainingExecution,
-                {
-                    "training_hash": self.training_hash,
-                    "model_type": training_type,
-                    "group": self.group,
-                    "login": self.credentials[0],
-                    "password": self.credentials[1],
-                    "url": self.base_url,
-                    "run_name": run_name,
-                    "python_version": python_version,
-                    "description": description,
-                    "features_file": features_file,
-                    "features_hash": features_hash,
-                    "target_file": target_file,
-                    "target_hash": target_hash,
-                    "output_file": output_file,
-                    "output_hash": output_hash,
-                    "metrics_file": metrics_file,
-                    "parameters_file": parameters_file,
-                    "model_file": model_file,
-                    "requirements_file": requirements_file,
-                    "wait_complete": wait_complete,
-                },
-            ),
-        }
-
-        builder_train_class, params = builder[training_type]
-        train = builder_train_class(**params)
-        return train
-
-    def get_training_execution(self, exec_id: Optional[str] = None):
-        """
-        Get the execution instance.
-
-        Parameters
-        ---------
-        exec_id: Optional[str], optional
-            Execution id. If not informed we get the last execution.
-
-        Returns
-        -------
-        MLOpsExecution
-            The chosen execution
-        """
-        raise NotImplementedError("Get training execution not implemented.")
-
-    @contextmanager
-    def log_train(
-        self,
-        *,
-        name,
-        X_train,
-        y_train,
-        description: Optional[str] = None,
-        save_path: Optional[str] = None,
-    ):
-        """
-        Creates a context manager that logs training progress.
-
-        name: str
-            Run name
-        X_train: DataFrame
-            Features
-        y_train: DataFrame
-            Target
-        description: str, default=None
-            Description
-        save_path: str, default=None
-            Path to save the trained model
-
-        Returns
-        -------
-        MLOpsTrainingLogger
-        """
-        try:
-            self.trainer = MLOpsTrainingLogger(
-                name=name,
-                X_train=X_train,
-                y_train=y_train,
-                save_path=save_path,
-            )
-            yield self.trainer
-
-        finally:
-            self.trainer._processing_logging_inputs()
-            self.run_training(
-                run_name=self.trainer.name,
-                training_type="External",
-                features_file=self.trainer.features_file,
-                target_file=self.trainer.target_file,
-                output_file=self.trainer.output_file,
-                metrics_file=self.trainer.metrics_file,
-                parameters_file=self.trainer.params_file,
-                model_file=self.trainer.model_file,
-                requirements_file=self.trainer.requirements,
-                description=description,
-                python_version=self.trainer.python_version,
-                extra_files=self.trainer.extras,
-            )
-            logger.info(
-                "Use the `get_training_execution()` method to get a training execution."
-            )
-
-
-class MLOpsTrainingClient(BaseMLOpsClient):
-    """
-    Class for client for accessing MLOps and manage models
-
-    Parameters
-    ----------
-    login: str
-        Login for authenticating with the client. You can also use the env variable MLOPS_USER to set this
-    password: str
-        Password for authenticating with the client. You can also use the env variable MLOPS_PASSWORD to set this
-    url: str
-        URL to MLOps Server. The default value is https://neomaril.datarisk.net, use it to test your deployment first before changing to production. You can also use the env variable MLOPS_URL to set this
-
-    Raises
-    ------
-    AuthenticationError
-        Invalid credentials
-    ServerError
-        Server unavailable
-    """
-
-    def __repr__(self) -> str:
-        return f"Codex version {constants.CODEX_VERSION}"
-
-    def __str__(self):
-        return f"Codex version {constants.CODEX_VERSION}"
-
-    def list(self, mode="dict"):
-        url = f"{self.base_url}/v2/training"
-        token = refresh_token(*self.credentials, self.base_url)
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            headers={
-                "Authorization": f"Bearer {token}",
-                "Neomaril-Origin": "Codex",
-                "Neomaril-Method": self.list.__qualname__,
-            },
-        )
-
-        if mode == "dict":
-            return response.json()
-        if mode == "count":
-            return len(response.json())
-        if mode == "log":
-            yaml_data = parse_json_to_yaml(response.json())
-            print(yaml_data)
-            return
-        raise InputError(f'{mode} is invalid. The options are "count", "dict" or "log"')
-
-    def get_training(
-        self, *, training_hash: str, group: str
-    ) -> MLOpsTrainingExperiment:
-        """
-        Acess a model using its id
-
-        Parameters
-        ---------
-        training_hash: str
-            Training id (hash) that needs to be acessed
-        group: str
-            Group the model is inserted.
-
-        Raises
-        ------
-        TrainingError
-            Model unavailable
-        ServerError
-            Unknown return from server
-
-        Returns
-        -------
-        MLOpsTrainingExperiment
-            A MLOpsTrainingExperiment instance with the training hash from `training_id`
-
-        Example
-        -------
-        >>> client = MLOpsTrainingClient()
-        >>> training = client.get_training('<TRAINING_HASH>', '<GROUP>')
-        """
-
-        return MLOpsTrainingExperiment(
-            training_hash=training_hash,
-            login=self.credentials[0],
-            password=self.credentials[1],
-            group=group,
-            url=self.base_url,
-        )
-
-    def __get_repeated_thash(
-        self, model_type: str, experiment_name: str, group: str
-    ) -> Union[str, None]:
-        """Look for a previous train experiment.
-
-        Args:
-            experiment_name (str): name given to the training, should be not null, case-sensitive, have between 3 and 32 characters,
-                                   that could be alphanumeric including accentuation (for example: 'é', à', 'ç','ñ') and space,
-                                   without blank spaces and special characters
-
-            model_type (str): type of the model being trained. It can be
-                                Classification: for ML algorithms related to classification (predicts discrete class labels) problems;
-                                Regression: the ones that will use regression (predict a continuous quantity) algorithms;
-                                Unsupervised: for training that will use ML algorithms without supervision.
-
-            group (str): name of the group, previously created, where the training will be inserted
-
-        Raises:
-            InputError: some part of the data is incorrect
-            AuthenticationError: user has insufficient permissions
-            ServerError: server is not available
-            Exception: generated exception in case of the response to the request is different from 201
-
-        Returns:
-            str | None: THash if it is found, otherwise, None is returned
-        """
-        url = f"{self.base_url}/training/search"
-        token = refresh_token(*self.credentials, self.base_url)
-
-        response = make_request(
-            url=url,
-            method="GET",
-            success_code=200,
-            headers={
-                "Authorization": f"Bearer {token}",
-            },
-        )
-
-        results = response.json().get("Results")
-        for result in results:
-            condition = (
-                result["ExperimentName"] == experiment_name
-                and result["GroupName"] == group
-                and result["ModelType"] == model_type
-            )
-            if condition:
-                logger.info("Found experiment with same attributes...")
-                return result["TrainingHash"]
-
-    def __register_training(
-        self, experiment_name: str, model_type: str, group: str
-    ) -> str:
-        """Creates a train experiment. A train experiment can aggregate multiple training runs (also called executions).
-        Each execution can eventually become a deployed model or not.
-
-        Args:
-            experiment_name (str): name given to the training, should be not null, case-sensitive, have between 3 and 32 characters,
-                                   that could be alphanumeric including accentuation (for example: 'é', à', 'ç','ñ') and space,
-                                   without blank spaces and special characters
-
-            model_type (str): type of the model being trained. It can be
-                                Classification: for ML algorithms related to classification (predicts discrete class labels) problems;
-                                Regression: the ones that will use regression (predict a continuous quantity) algorithms;
-                                Unsupervised: for training that will use ML algorithms without supervision.
-
-            group (str): name of the group, previous created, where the training will be inserted
-
-        Raises:
-            InputError: some part of the data is incorrect
-            AuthenticationError: user has insufficient permissions
-            ServerError: server is not available
-            Exception: generated exception in case of the response to the request is different from 201
-
-        Returns:
-            str: training hash of the experiment
-        """
-        url = f"{self.base_url}/v2/training/{group}"
-        token = refresh_token(*self.credentials, self.base_url)
-
-        payload = {"Name": experiment_name, "Type": model_type}
-
-        response = make_request(
-            url=url,
-            method="POST",
-            success_code=201,
-            json=payload,
-            headers={
-                "Authorization": f"Bearer {token}",
-                "Neomaril-Origin": "Codex",
-                "Neomaril-Method": self.__register_training.__qualname__,
-            },
-        )
-
-        response_data = response.json()
-        logger.info(response_data["Message"])
-        training_hash = response_data["TrainingHash"]
-        return training_hash
-
-    def create_training_experiment(
-        self,
-        *,
-        experiment_name: str,
-        model_type: str,
-        group: str,
-        force: Optional[bool] = False,
-    ) -> MLOpsTrainingExperiment:
-        """
-        Create a new training experiment on MLOps.
-
-        Parameters
-        ---------
-        experiment_name: str
-            The name of the experiment, in less than 32 characters
-        model_type: str
-            The name of the scoring function inside the source file.
-        group: str
-            Group the model is inserted. Default to 'datarisk' (public group)
-        force: Optional[bool], optional
-            Forces to create a new training with the same model_type, experiment_name, group
-
-        Raises
-        ------
-        InputError
-            Some input parameters its invalid
-        ServerError
-            Unknow internal server error
-
-        Returns
-        -------
-        MLOpsTrainingExperiment
-            A MLOpsTrainingExperiment instance with the training hash from `training_id`
-
-        Example
-        -------
-        >>> training = client.create_training_experiment('Training example', 'Classification', 'ex_group')
-        """
-
-        validate_group_existence(group, self)
-
-        if model_type not in ["Classification", "Regression", "Unsupervised"]:
-            raise InputError(
-                f"Invalid model_type {model_type}. Should be one of the following: Classification, Regression or "
-                f"Unsupervised"
-            )
-
-        logger.info("Trying to load experiment...")
-        training_hash = self.__get_repeated_thash(
-            model_type=model_type, experiment_name=experiment_name, group=group
-        )
-
-        if force or training_hash is None:
-            msg = (
-                "The experiment you're creating has identical name, group, and model type attributes to an existing one. "
-                + "Since forced creation is active, we will continue with the process as specified"
-                if force
-                else "Could not find experiment. Creating a new one..."
-            )
-            logger.info(msg)
-            training_hash = self.__register_training(
-                experiment_name=experiment_name, model_type=model_type, group=group
-            )
-
-        return MLOpsTrainingExperiment(
-            training_hash=training_hash,
-            login=self.credentials[0],
-            password=self.credentials[1],
-            group=group,
-            url=self.base_url,
-        )
diff --git a/src/mlops_codex/trainingv2/training_executions.py b/src/mlops_codex/trainingv2/training_executions.py
deleted file mode 100644
index 50d61e0..0000000
--- a/src/mlops_codex/trainingv2/training_executions.py
+++ /dev/null
@@ -1,783 +0,0 @@
-from typing import Union
-
-from pydantic import model_validator
-
-from mlops_codex.__utils import parse_dict_or_file
-from mlops_codex.dataset import MLOpsDataset, validate_dataset
-from mlops_codex.exceptions import InputError
-from mlops_codex.http_request_handler import make_request, refresh_token
-from mlops_codex.logger_config import get_logger
-from mlops_codex.shared.utils import parse_data
-from mlops_codex.trainingv2.base import ITrainingExecution
-from mlops_codex.trainingv2.trigger import (
-    trigger_automl_training,
-    trigger_custom_training,
-    trigger_external_training,
-)
-from mlops_codex.trainingv2.validations import validate_input
-from mlops_codex.validations import file_extension_validation
-
-logger = get_logger()
-
-
-class CustomTrainingExecution(ITrainingExecution):
-    """
-    Custom training execution class.
-
-    Parameters
-    ----------
-    training_hash: str
-        Training hash.
-    group: str
-        Group where the training is inserted.
-    model_type: str
-        Type of the model.
-    execution_id: int
-        Execution ID of a training.
-    experiment_name: str
-        Name of the experiment.
-    login: str
-        Login credential.
-    password: str
-        Password credential.
-    url: str
-        URL used to connect to the MLOps server.
-    mlops_class: BaseMLOps
-        MLOps class instance.
-    """
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate(cls, values):
-        """
-        Validates the input values for custom training execution.
-
-        Parameters
-        ----------
-        values: dict
-            Dictionary of input values.
-
-        Returns
-        -------
-        dict
-            Validated input values.
-        """
-
-        logger.info("Validating data...")
-
-        fields_required = (
-            "input_data",
-            "upload_data",
-            "run_name",
-            "source_file",
-            "requirements_file",
-            "training_reference",
-            "python_version",
-        )
-
-        validate_input(fields_required, values)
-
-        source_file = values["source_file"]
-        file_extension_validation(source_file, {"py", "ipynb"})
-
-        requirements_file = values["requirements_file"]
-        file_extension_validation(requirements_file, {"txt"})
-
-        keys = (
-            "training_hash",
-            "group",
-            "model_type",
-            "login",
-            "password",
-            "url",
-        )
-
-        data = {key: values[key] for key in keys}
-
-        return data
-
-    def _promote(self, source_file_path: str, schema_path: str, operation: str, model_name: str, input_type: str, model_reference: str):
-        """
-        Abstract method to promote the execution.
-
-        Parameters
-        ----------
-        source_file_path: str
-            A python script with an entry point function. It needs to return a dict, a list of dicts or a JSON string
-        schema_path: str
-            A JSON, CSV or PARQUET file with a sample of the input for the entry point function
-        operation: str
-            Defines how the model will be treated at the API. It can be: Sync or Async
-        input_type: str
-            The type of the input that the model expects, for example, .csv
-        model_name: str
-            Corresponds to the name of the model
-        model_reference: str
-            The name of the entry point function at the source file
-        Returns
-        -------
-        str
-            Model hash
-        """
-        user_token = refresh_token(
-            *self.mlops_class.credentials, self.mlops_class.base_url
-        )
-        response = make_request(
-            url=f"{self.url}/v2/training/execution/{self.execution_id}/promote",
-            method="PATCH",
-            success_code=201,
-            headers={
-                "Authorization": f"Bearer {user_token}"
-            },
-            files={
-                "source": open(source_file_path, "rb"),
-                "schema": open(schema_path, "rb"),
-            },
-            data={
-                "operation": operation,
-                "input_type": input_type,
-                "name": model_name,
-                "model_reference": model_reference,
-            }
-        )
-
-        msg = response.json()["Message"]
-        logger.info(msg)
-
-        model_hash = response.json()["ModelHash"]
-        logger.info(f"Model hash: {model_hash}")
-        return model_hash
-
-    def __init__(self, **data):
-        super().__init__(**data)
-
-        if data.get("is_copy", False):
-            return
-
-        user_token = refresh_token(
-            *self.mlops_class.credentials, self.mlops_class.base_url
-        )
-
-        self.execution_id = trigger_custom_training(
-            url=self.mlops_class.base_url,
-            token=user_token,
-            training_hash=self.training_hash,
-            run_name=data["run_name"],
-            description=data["description"],
-            input_data=data["input_data"],
-            upload_data=data["upload_data"],
-            requirements_file=data["requirements_file"],
-            source_file=data["source_file"],
-            training_reference=data["training_reference"],
-            python_version=data["python_version"],
-            extra_files=data["extra_files"],
-            env=data["env"],
-        )
-
-        self.host()
-
-        if data["wait_complete"]:
-            self.wait_ready()
-
-    @classmethod
-    def _do_copy(cls, url, token, group, experiment_name, mlops_class, **kwargs):
-        """
-        Abstract method to copy the execution.
-
-        Parameters
-        ----------
-        url: str
-            URL to copy the execution.
-        token: str
-            Authentication token.
-        group: str
-            Group where the training is inserted.
-        experiment_name: str
-            Name of the experiment.
-        mlops_class: BaseMLOps
-            MLOps class instance.
-        kwargs: dict
-            Extra arguments passed to the specific function.
-        """
-        response = make_request(
-            url=url,
-            method="POST",
-            success_code=201,
-            headers={"Authorization": f"Bearer {token}"},
-        ).json()
-
-        logger.info(response["Message"])
-
-        fields = dict(
-            training_hash=response["TrainingHash"],
-            group=group,
-            model_type="Custom",
-            execution_id=response["ExecutionId"],
-            experiment_name=experiment_name,
-            login=mlops_class.credentials[0],
-            password=mlops_class.credentials[1],
-            url=mlops_class.base_url,
-            mlops_class=mlops_class,
-            is_copy=True,
-        )
-
-        new_execution = cls.model_construct(**fields)
-
-        new_execution._update_execution(
-            token=token,
-            source_file=kwargs.get("source_file"),
-            training_reference=kwargs.get("training_reference"),
-            python_version=kwargs.get("python_version"),
-            train_data=kwargs.get("train_data"),
-            dataset_name=kwargs.get("dataset_name", "input"),
-            dataset=kwargs.get("dataset"),
-            requirements_file=kwargs.get("requirements_file"),
-            extra_files=kwargs.get("extra_files", []),
-            env=kwargs.get("env"),
-            wait_complete=kwargs.get("wait_complete"),
-        )
-
-        return new_execution
-
-    def _update_execution(
-        self,
-        token: str,
-        source_file: str = None,
-        training_reference: str = None,
-        python_version: str = None,
-        train_data: str = None,
-        dataset_name: str = "input",
-        dataset: Union[str, MLOpsDataset] = None,
-        requirements_file: str = None,
-        extra_files: str = None,
-        env: str = None,
-        wait_complete: bool = True,
-    ):
-        """
-        Updates the execution with new parameters.
-
-        Parameters
-        ----------
-        source_file : str, optional
-            Path to the source code file, by default None
-        training_reference : str, optional
-            Training reference identifier, by default None
-        python_version : str, optional
-            Python version to use, by default None
-        requirements_file : str, optional
-            Path to requirements.txt file, by default None
-        extra_files : str, optional
-            List of additional files to include, by default None
-        env : str, optional
-            Environment variables, by default None
-        wait_complete : bool, optional
-            Whether to wait for execution completion, by default None
-
-        Raises
-        ------
-        TrainingError
-            If the execution is not in requested state.
-        """
-
-        if dataset is not None:
-            dataset_hash = validate_dataset(dataset)
-        else:
-            dataset_hash = None
-
-        input_data, upload_data = parse_data(
-            file_path=train_data,
-            form_data="dataset_hash" if dataset_hash is not None else "dataset_name",
-            file_name=dataset_name,
-            file_form="input",
-            dataset_hash=dataset_hash,
-        )
-
-        self.execution_id = trigger_custom_training(
-            url=self.mlops_class.base_url,
-            token=token,
-            execution_id=self.execution_id,
-            input_data=input_data,
-            upload_data=upload_data,
-            requirements_file=requirements_file,
-            source_file=source_file,
-            training_reference=training_reference,
-            python_version=python_version,
-            extra_files=extra_files,
-            env=env,
-        )
-
-        self.host()
-
-        if wait_complete:
-            self.wait_ready()
-
-
-class AutoMLTrainingExecution(ITrainingExecution):
-    """
-    AutoML training execution class.
-
-    Parameters
-    ----------
-    training_hash: str
-        Training hash.
-    group: str
-        Group where the training is inserted.
-    model_type: str
-        Type of the model.
-    execution_id: int
-        Execution ID of a training.
-    experiment_name: str
-        Name of the experiment.
-    login: str
-        Login credential.
-    password: str
-        Password credential.
-    url: str
-        URL used to connect to the MLOps server.
-    mlops_class: BaseMLOps
-        MLOps class instance.
-    """
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate(cls, values):
-        """
-        Validates the input values for AutoML training execution.
-
-        Parameters
-        ----------
-        values: dict
-            Dictionary of input values.
-
-        Returns
-        -------
-        dict
-            Validated input values.
-        """
-
-        validate_input({"input_data", "upload_data", "conf_dict", "run_name"}, values)
-
-        file_extension_validation(values["conf_dict"], {"json"})
-
-        keys = (
-            "training_hash",
-            "group",
-            "model_type",
-            "login",
-            "password",
-            "url",
-        )
-
-        data = {key: values[key] for key in keys}
-
-        return data
-
-    def _promote(self, model_name: str, input_type: str, operation: str, schema_path: str):
-        """
-        Abstract method to promote the execution.
-
-        Parameters
-        ----------
-        schema_path: str
-            A JSON, CSV or PARQUET file with a sample of the input for the entry point function
-        operation: str
-            Defines how the model will be treated at the API. It can be: Sync or Async
-        model_name: str
-            Corresponds to the name of the model
-        input_type: str
-            Type of the input that the model expects. Must be CSV or Parquet
-        Returns
-        -------
-        str
-            Model hash
-        """
-
-        raise NotImplementedError()
-
-    def __init__(self, **data):
-        super().__init__(**data)
-
-        if data.get("is_copy", False):
-            return
-
-        user_token = refresh_token(
-            *self.mlops_class.credentials, self.mlops_class.base_url
-        )
-
-        self.execution_id = trigger_automl_training(
-            url=self.mlops_class.base_url,
-            token=user_token,
-            training_hash=self.training_hash,
-            run_name=data["run_name"],
-            description=data["description"],
-            input_data=data["input_data"],
-            upload_data=data["upload_data"],
-            conf_dict=data["conf_dict"],
-            extra_files=data["extra_files"],
-        )
-
-        self.host()
-
-        if data["wait_complete"]:
-            self.wait_ready()
-
-    @classmethod
-    def _do_copy(cls, url, token, group, experiment_name, mlops_class, **kwargs):
-        """
-        Abstract method to copy the execution.
-
-        Parameters
-        ----------
-        url: str
-            URL to copy the execution.
-        token: str
-            Authentication token.
-        group: str
-            Group where the training is inserted.
-        experiment_name: str
-            Name of the experiment.
-        mlops_class: BaseMLOps
-            MLOps class instance.
-        kwargs: dict
-            Extra arguments passed to the specific function.
-        """
-        response = make_request(
-            url=url,
-            method="POST",
-            success_code=200,
-            headers={"Authorization": f"Bearer {token}"},
-        ).json()
-
-        fields = dict(
-            training_hash=response["TrainingHash"],
-            group=group,
-            model_type="AutoML",
-            execution_id=response["ExecutionId"],
-            experiment_name=experiment_name,
-            login=mlops_class.credentials[0],
-            password=mlops_class.credentials[1],
-            url=mlops_class.base_url,
-            mlops_class=mlops_class,
-            is_copy=True,
-        )
-
-        new_execution = cls.model_construct(**fields)
-
-        new_execution._update_execution(
-            conf_dict=kwargs.get("conf_dict"),
-            extra_files=kwargs.get("extra_files", []),
-            train_data=kwargs.get("train_data"),
-            dataset_name=kwargs.get("dataset_name", "input"),
-            dataset=kwargs.get("dataset"),
-            wait_complete=kwargs.get("wait_complete"),
-        )
-
-    def _update_execution(
-        self,
-        conf_dict: str = None,
-        train_data: str = None,
-        dataset_name: str = "input",
-        dataset: Union[str, MLOpsDataset] = None,
-        extra_files: str = None,
-        wait_complete: bool = True,
-    ):
-        if dataset is not None:
-            dataset_hash = validate_dataset(dataset)
-        else:
-            dataset_hash = None
-
-        input_data, upload_data = parse_data(
-            file_path=train_data,
-            form_data="dataset_hash" if dataset_hash is not None else "dataset_name",
-            file_name=dataset_name,
-            file_form="input",
-            dataset_hash=dataset_hash,
-        )
-
-        self.execution_id = trigger_automl_training(
-            execution_id=self.execution_id,
-            url=self.mlops_class.base_url,
-            token=refresh_token(
-                *self.mlops_class.credentials, self.mlops_class.base_url
-            ),
-            input_data=input_data,
-            upload_data=upload_data,
-            conf_dict=parse_dict_or_file(conf_dict),
-            extra_files=extra_files,
-        )
-
-        self.host()
-
-        if wait_complete:
-            self.wait_ready()
-
-
-class ExternalTrainingExecution(ITrainingExecution):
-    """
-    External training execution class.
-
-    Parameters
-    ----------
-    training_hash: str
-        Training hash.
-    group: str
-        Group where the training is inserted.
-    model_type: str
-        Type of the model.
-    execution_id: int
-        Execution ID of a training.
-    experiment_name: str
-        Name of the experiment.
-    login: str
-        Login credential.
-    password: str
-        Password credential.
-    url: str
-        URL used to connect to the MLOps server.
-    mlops_class: BaseMLOps
-        MLOps class instance.
-    """
-
-    @model_validator(mode="before")
-    @classmethod
-    def validate(cls, values):
-        """
-        Validates the input values for External training execution.
-
-        Parameters
-        ----------
-        values: dict
-            Dictionary of input values.
-
-        Returns
-        -------
-        dict
-            Validated input values.
-        """
-        logger.info("Validating external training execution...")
-
-        copy_dict = {
-            "run_name": values["run_name"],
-            "features": (
-                values.get("features_file")
-                if values.get("features_file")
-                else values.get("features_hash")
-            ),
-            "target": (
-                values.get("target_file")
-                if values.get("target_file")
-                else values.get("target_hash")
-            ),
-            "output": (
-                values.get("output_file")
-                if values.get("output_file")
-                else values.get("output_hash")
-            ),
-        }
-
-        validate_input({"run_name", "features", "target", "output"}, copy_dict)
-
-        if values["features_file"] and values["features_hash"]:
-            raise InputError("You must provide either features file or dataset hash.")
-
-        if values["output_file"] and values["output_hash"]:
-            raise InputError("You must provide either output file or dataset hash.")
-
-        if values["target_file"] and values["target_hash"]:
-            raise InputError("You must provide either target file or dataset hash.")
-
-        if values["requirements_file"]:
-            file_extension_validation(values["requirements_file"], {"txt"})
-
-        keys = (
-            "training_hash",
-            "group",
-            "model_type",
-            "login",
-            "password",
-            "url",
-        )
-
-        data = {key: values[key] for key in keys}
-
-        return data
-
-    def __init__(self, **data):
-        super().__init__(**data)
-
-        if data.get("is_copy", False):
-            return
-
-        user_token = refresh_token(
-            *self.mlops_class.credentials, self.mlops_class.base_url
-        )
-
-        self.execution_id = trigger_external_training(
-            url=self.mlops_class.base_url,
-            token=user_token,
-            training_hash=self.training_hash,
-            run_name=data["run_name"],
-            description=data["description"],
-            features_file=data["features_file"],
-            features_hash=data["features_hash"],
-            target_file=data["target_file"],
-            target_hash=data["target_hash"],
-            output_file=data["output_file"],
-            output_hash=data["output_hash"],
-            metrics_file=data["metrics_file"],
-            parameters_file=data["parameters_file"],
-            model_file=data["model_file"],
-            requirements_file=data["requirements_file"],
-            python_version=data["python_version"],
-        )
-
-        self.host()
-
-        if data["wait_complete"]:
-            self.wait_ready()
-
-    def _promote(self, *args, **kwargs):
-        """
-        Abstract method to promote the execution.
-
-        Parameters
-        ----------
-        args: tuple
-            Positional arguments.
-        kwargs: dict
-            Keyword arguments.
-
-        Returns
-        -------
-        None
-        """
-
-        raise NotImplementedError("Promotion is not implemented.")
-
-    @classmethod
-    def _do_copy(cls, url, token, group, experiment_name, mlops_class, **kwargs):
-        """
-        Abstract method to copy the execution.
-
-        Parameters
-        ----------
-        url: str
-            URL to copy the execution.
-        token: str
-            Authentication token.
-        group: str
-            Group where the training is inserted.
-        experiment_name: str
-            Name of the experiment.
-        mlops_class: BaseMLOps
-            MLOps class instance.
-        kwargs: dict
-            Extra arguments passed to the specific function.
-        """
-        response = make_request(
-            url=url,
-            method="POST",
-            success_code=200,
-            headers={"Authorization": f"Bearer {token}"},
-        ).json()
-
-        fields = dict(
-            training_hash=response["TrainingHash"],
-            group=group,
-            model_type="External",
-            execution_id=response["ExecutionId"],
-            experiment_name=experiment_name,
-            login=mlops_class.credentials[0],
-            password=mlops_class.credentials[1],
-            url=mlops_class.base_url,
-            mlops_class=mlops_class,
-            is_copy=True,
-        )
-
-        new_training = cls.model_construct(**fields)
-        new_training._update_execution(
-            features_file=kwargs.get("features_file"),
-            features_hash=kwargs.get("features_hash"),
-            target_file=kwargs.get("target_file"),
-            target_hash=kwargs.get("target_hash"),
-            output_file=kwargs.get("output_file"),
-            output_hash=kwargs.get("output_hash"),
-            metrics_file=kwargs.get("metrics_file"),
-            parameters_file=kwargs.get("parameters_file"),
-            model_file=kwargs.get("model_file"),
-            requirements_file=kwargs.get("requirements_file"),
-            python_version=kwargs.get("python_version", "3.10"),
-            wait_complete=kwargs.get("wait_complete"),
-        )
-
-        return new_training
-
-    def _update_execution(
-        self,
-        features_file: str = None,
-        features_hash: str = None,
-        target_file: str = None,
-        target_hash: str = None,
-        output_file: str = None,
-        output_hash: str = None,
-        metrics_file: str = None,
-        parameters_file: str = None,
-        model_file: str = None,
-        requirements_file: str = None,
-        python_version: str = None,
-        wait_complete: bool = True,
-    ):
-        """
-        Updates the execution with new parameters.
-
-        Parameters
-        ----------
-        features_file : str, optional
-            Path to features file, by default None
-        features_hash : str, optional
-            Features dataset hash, by default None
-        target_file : str, optional
-            Path to target file, by default None
-        target_hash : str, optional
-            Target dataset hash, by default None
-        output_file : str, optional
-            Path to output file, by default None
-        output_hash : str, optional
-            Output dataset hash, by default None
-        metrics_file : str, optional
-            Path to metrics file, by default None
-        parameters_file : str, optional
-            Path to parameters file, by default None
-        model_file : str, optional
-            Path to model file, by default None
-        requirements_file : str, optional
-            Path to requirements file, by default None
-        python_version : str, optional
-            Python version to use, by default None
-        wait_complete : bool, optional
-            Whether to wait for execution completion, by default True
-        """
-
-        self.execution_id = trigger_external_training(
-            execution_id=self.execution_id,
-            url=self.mlops_class.base_url,
-            token=refresh_token(
-                *self.mlops_class.credentials, self.mlops_class.base_url
-            ),
-            features_file=features_file,
-            features_hash=features_hash,
-            target_file=target_file,
-            target_hash=target_hash,
-            output_file=output_file,
-            output_hash=output_hash,
-            metrics_file=metrics_file,
-            parameters_file=parameters_file,
-            model_file=model_file,
-            requirements_file=requirements_file,
-            python_version=python_version,
-        )
-
-        self.host()
-
-        if wait_complete:
-            self.wait_ready()
diff --git a/src/mlops_codex/trainingv2/trigger.py b/src/mlops_codex/trainingv2/trigger.py
deleted file mode 100644
index d180e99..0000000
--- a/src/mlops_codex/trainingv2/trigger.py
+++ /dev/null
@@ -1,303 +0,0 @@
-from mlops_codex.logger_config import get_logger
-from mlops_codex.shared.data_transmitter import send_file, send_json
-from mlops_codex.shared.utils import parse_data
-from mlops_codex.trainingv2.commons import register_execution
-from mlops_codex.validations import validate_python_version
-
-logger = get_logger()
-
-
-def trigger_custom_training(**kwargs: dict) -> int:
-    """
-    Triggers a custom training execution.
-
-    Parameters
-    ----------
-    **kwargs : dict
-        Dictionary containing:
-        - url : str
-            Base URL for the API
-        - token : str
-            Authentication token
-        - training_hash : str
-            Training hash identifier
-        - run_name : str
-            Name of the run
-        - description : str
-            Description of the training
-        - input_data : dict
-            Input data dictionary
-        - upload_data : dict
-            Upload data dictionary
-        - requirements_file : str
-            Path to requirements file
-        - source_file : str
-            Path to source file
-        - training_reference : str
-            Training reference
-        - python_version : str
-            Python version to use
-        - extra_files : list, optional
-            List of tuples containing (name, path) for extra files
-        - env : str, optional
-            Path to env file
-
-    Returns
-    -------
-    int
-        Execution ID
-    """
-
-    execution_id = (
-        kwargs.get("execution_id")
-        if kwargs.get("execution_id", None) is not None
-        else register_execution(
-            url=f"{kwargs['url']}/v2/training/{kwargs['training_hash']}/execution",
-            token=kwargs["token"],
-            run_name=kwargs["run_name"],
-            description=kwargs["description"],
-            training_type="Custom",
-        )
-    )
-
-    if kwargs["input_data"] is not None and kwargs["upload_data"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/input/file",
-            token=kwargs["token"],
-            input_data=kwargs["input_data"],
-            upload_data=kwargs["upload_data"],
-            neomaril_method="Upload input",
-        )
-
-    if kwargs["requirements_file"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/requirements-file",
-            token=kwargs["token"],
-            upload_data={"requirements": open(kwargs["requirements_file"], "rb")},
-            neomaril_method="Upload requirements file",
-        )
-
-    if all(
-        kwargs[key] for key in ["source_file", "training_reference", "python_version"]
-    ):
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/script-file",
-            token=kwargs["token"],
-            input_data={
-                "training_reference": kwargs["training_reference"],
-                "python_version": validate_python_version(kwargs["python_version"]),
-            },
-            upload_data={"script": open(kwargs["source_file"], "rb")},
-            neomaril_method="Upload script",
-        )
-
-    for name, path in kwargs.get("extra_files", []):
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/extra-file",
-            token=kwargs["token"],
-            input_data={"file_name": name},
-            upload_data={"extra": open(path, "rb")},
-            neomaril_method="Upload extra file",
-        )
-
-    if kwargs["env"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/env/file",
-            token=kwargs["token"],
-            upload_data={"env": open(kwargs["env"], "rb")},
-            neomaril_method="Upload env",
-        )
-
-    return execution_id
-
-
-def trigger_automl_training(**kwargs) -> int:
-    """
-    Triggers an AutoML training execution.
-
-    Parameters
-    ----------
-    **kwargs : dict
-        Dictionary containing:
-        - url : str
-            Base URL for the API
-        - token : str
-            Authentication token
-        - training_hash : str
-            Training hash identifier
-        - run_name : str
-            Name of the run
-        - description : str
-            Description of the training
-        - conf_dict : str
-            Path to configuration dictionary
-        - input_data : dict
-            Input data dictionary
-        - upload_data : dict
-            Upload data dictionary
-        - extra_files : list, optional
-            List of tuples containing (name, path) for extra files
-
-    Returns
-    -------
-    int
-        Execution ID
-    """
-    execution_id = (
-        kwargs.get("execution_id")
-        if kwargs.get("execution_id", None) is not None
-        else register_execution(
-            url=f"{kwargs['url']}/v2/training/{kwargs['training_hash']}/execution",
-            token=kwargs["token"],
-            run_name=kwargs["run_name"],
-            description=kwargs["description"],
-            training_type="AutoML",
-        )
-    )
-
-    if kwargs["conf_dict"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/conf-dict/file",
-            token=kwargs["token"],
-            input_data=None,
-            upload_data={"conf_dict": open(kwargs["conf_dict"], "rb")},
-            neomaril_method="Upload conf_dict",
-        )
-
-    if kwargs["input_data"] is not None and kwargs["upload_data"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/input/file",
-            token=kwargs["token"],
-            input_data=kwargs["input_data"],
-            upload_data=kwargs["upload_data"],
-            neomaril_method="Upload input",
-        )
-
-    for name, path in kwargs.get("extra_files", []):
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/extra-file",
-            token=kwargs["token"],
-            input_data={"file_name": name},
-            upload_data={"extra": open(path, "rb")},
-            neomaril_method="Upload extra file",
-        )
-
-    return execution_id
-
-
-def trigger_external_training(**kwargs) -> int:
-    """
-    Triggers an external training execution.
-
-    Parameters
-    ----------
-    **kwargs : dict
-        Dictionary containing:
-        - url : str
-            Base URL for the API
-        - token : str
-            Authentication token
-        - training_hash : str
-            Training hash identifier
-        - data : dict
-            Dictionary containing features, target and output file paths and hashes
-        - run_name : str
-            Name of the run
-        - description : str
-            Description of the training
-        - python_version : str
-            Python version to use
-        - metrics_file : str, optional
-            Path to metrics file
-        - parameters_file : str, optional
-            Path to parameters file
-        - model_file : str, optional
-            Path to model file
-        - requirements_file : str, optional
-            Path to requirements file
-
-    Returns
-    -------
-    int
-        Execution ID
-    """
-    execution_id = (
-        kwargs.get("execution_id")
-        if kwargs.get("execution_id", None) is not None
-        else register_execution(
-            url=f"{kwargs['url']}/v2/training/{kwargs['training_hash']}/execution",
-            token=kwargs["token"],
-            run_name=kwargs["run_name"],
-            description=kwargs["description"],
-            training_type="External",
-        )
-    )
-
-    for var in ["features", "target", "output"]:
-        file_path = kwargs.get(f"{var}_file")
-        dataset_hash = kwargs.get(f"{var}_hash")
-
-        if file_path is None and dataset_hash is None:
-            continue
-
-        form_data, upload_data = parse_data(
-            file_path=file_path,
-            form_data="dataset_name" if file_path else "dataset_hash",
-            file_name=var if file_path else dataset_hash,
-            file_form=var if file_path else None,
-            dataset_hash=dataset_hash,
-        )
-
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/{var}/file",
-            token=kwargs["token"],
-            input_data=form_data,
-            upload_data=upload_data,
-            neomaril_method=f"Upload {var} file",
-        )
-
-    if kwargs["metrics_file"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/metrics/file",
-            token=kwargs["token"],
-            upload_data={"metrics": open(kwargs["metrics_file"], "rb")},
-            neomaril_method="Upload metrics file",
-        )
-
-    if kwargs["parameters_file"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/parameters/file",
-            token=kwargs["token"],
-            upload_data={"parameters": open(kwargs["parameters_file"], "rb")},
-            neomaril_method="Upload parameters file",
-        )
-
-    if kwargs["model_file"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/model/file",
-            token=kwargs["token"],
-            upload_data={"model": open(kwargs["model_file"], "rb")},
-            neomaril_method="Upload model file",
-        )
-
-    if kwargs["requirements_file"] is not None:
-        send_file(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/requirements-file",
-            token=kwargs["token"],
-            upload_data={"requirements": open(kwargs["requirements_file"], "rb")},
-            neomaril_method="Upload requirements file",
-        )
-
-    if kwargs["python_version"] is not None:
-        send_json(
-            url=f"{kwargs['url']}/v2/training/execution/{execution_id}/python-version",
-            token=kwargs["token"],
-            payload={
-                "PythonVersion": validate_python_version(
-                    python_version=kwargs["python_version"]
-                )
-            },
-            neomaril_method="Set python version",
-        )
-
-    return execution_id
diff --git a/src/mlops_codex/trainingv2/validations.py b/src/mlops_codex/trainingv2/validations.py
deleted file mode 100644
index ac2e277..0000000
--- a/src/mlops_codex/trainingv2/validations.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-Specific validators for training module
-"""
-
-from mlops_codex.exceptions import InputError
-
-
-def validate_input(required, fields):
-    if (not all(k in fields for k in required)) or (
-        not all(fields[f] for f in required)
-    ):
-        raise InputError(
-            f"The parameters {required} are mandatory for this training execution type."
-        )
-
-    return True

From 7eedc9fd6ae967668ad1eb6821c919dac1fb19a1 Mon Sep 17 00:00:00 2001
From: henrique-lh <henrique.santos.lhls@gmail.com>
Date: Wed, 9 Jul 2025 17:07:25 -0300
Subject: [PATCH 3/5] - update codex version

---
 src/mlops_codex/shared/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlops_codex/shared/constants.py b/src/mlops_codex/shared/constants.py
index 13fd00e..ed30924 100644
--- a/src/mlops_codex/shared/constants.py
+++ b/src/mlops_codex/shared/constants.py
@@ -1 +1 @@
-CODEX_VERSION = "2.2.10"
\ No newline at end of file
+CODEX_VERSION = "2.2.11"
\ No newline at end of file

From 31d50d2830d4e52282975688fc555da3b0ffa8b8 Mon Sep 17 00:00:00 2001
From: henrique-lh <henrique.santos.lhls@gmail.com>
Date: Wed, 9 Jul 2025 17:07:35 -0300
Subject: [PATCH 4/5] - add log

---
 src/mlops_codex/preprocessing.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/mlops_codex/preprocessing.py b/src/mlops_codex/preprocessing.py
index 68274b5..e1f7055 100644
--- a/src/mlops_codex/preprocessing.py
+++ b/src/mlops_codex/preprocessing.py
@@ -558,6 +558,7 @@ def create(
         logger.info("Requirements file uploaded")
 
         if env_file:
+            logger.info("Environment file uploaded")
             make_request(
                 url=f"{self.base_url}/v2/preprocessing/{preprocessing_script_hash}/env-file",
                 method='PATCH',

From 2a43b24745c0eddb7c8be291e2594f004f64225c Mon Sep 17 00:00:00 2001
From: henrique-lh <henrique.santos.lhls@gmail.com>
Date: Wed, 9 Jul 2025 18:05:27 -0300
Subject: [PATCH 5/5] - log after request is succeeded

---
 src/mlops_codex/preprocessing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/mlops_codex/preprocessing.py b/src/mlops_codex/preprocessing.py
index e1f7055..7a6d014 100644
--- a/src/mlops_codex/preprocessing.py
+++ b/src/mlops_codex/preprocessing.py
@@ -558,7 +558,6 @@ def create(
         logger.info("Requirements file uploaded")
 
         if env_file:
-            logger.info("Environment file uploaded")
             make_request(
                 url=f"{self.base_url}/v2/preprocessing/{preprocessing_script_hash}/env-file",
                 method='PATCH',
@@ -568,6 +567,7 @@ def create(
                     "Authorization": f"Bearer {token}",
                 },
             )
+            logger.info("Environment file uploaded")
 
         logger.info("Hosting preprocessing script")